Gesture-controlled, contactless electric switch.

Abstract

A button, a switch, a slider, or a lever… These are all basic physical control interfaces that an individual interacts with to open, close, and select between several active states of an electrical device. They are the basis for more sophisticated standard HMIs like keyboards or mice, which perform only a single mechanical action: for example, to turn on a light bulb, an individual presses a switch. They have become indispensable and are now found in many homes and public spaces. They are also present in most keypads, vending machines, and self-service kiosks. Despite their proven effectiveness, their use in public spaces raises concerns about hygiene and safety for those connected to higher-voltage electrical currents.The invention described below relates to a new type of contactor consisting of an optical device and a programmable electrical circuit. This assembly is designed with a wired and connected electrical architecture. The device analyzes a scene, searches for a specific skin-colored image, and then activates or deactivates, without contact, associated electrical control mechanisms such as switches, actuators, or electrical switches.

Description

Title of the invention: Contactless, gesture-controlled electrical switch.

[0001] This is a new device for direct interaction between a human being and an electrical or electronic interconnection network on which a flow of electric current circulates. It is associated with spatial and temporal information and can be used to replace certain conventional manual control devices for future applications.

[0002] Today, to interact with all the electrical devices in our daily environment, there are three approaches:

[0003] On the one hand, the classic approach, which consists of acting by pressure on a surface to move a contact device from a position A to a position B, thus making it possible to cut off or let the current (or any flow) pass. In some cases the contact action is direct, in others, the action is executed by means of a remote control or a module which exchanges information with an electrical system, which allow remote interaction with all the devices in our daily environment.

[0004] This approach, which has already proven its effectiveness, has two major drawbacks: the action resulting in intimate contact of the finger with the surface, then increases the risk of electrocution if the electrical switches are in a ground fault condition, and furthermore represents a significant potential danger of contamination in the event of an epidemic.

[0005] On the other hand, the approach of gestural interfaces, or human-machine interaction based on artificial vision. These electronic devices are equipped with one or more optical sensors that interact with a computer or powerful calculators. One or more image processing algorithms are implemented to detect the hand, segment it, recognize a sign, and then associate it with an action.

[0006] For better results it is possible to use instrumented gloves, equipped with numerous sensors measuring the angles of the joints and the position of the hand in space. Particularly used to control virtual actors, by designating and manipulating graphic objects on computer screens [Baudel93, Bellik96] they can also be used for sign language recognition [Braffort96]. Unfortunately, such gloves remain at the very least inaccessible because they are expensive and fragile, and their connecting cables constitute an obstacle.

[0007] Gesture recognition systems have been attempted, for example US patent 5,534,917. Other more recent techniques aim rather to implement Very robust algorithms, which can use Kalman filters with Gaussian assumptions, combined with the common image subtraction technique with the background image. This reference image is then updated automatically, the goal being to achieve consistent and real-time image processing. However, these systems generally involve complicated and intensive computer analysis and require expensive and bulky hardware.

[0008] Other systems use color properties for image analysis. The scene is segmented according to the skin color model. This gesture recognition system has special requirements for accurate capture of the shape and contour of the hand. This involves a uniform background, fixed lighting, high resolution (or multiple cameras) and considerable processing power. In addition, no extraneous movements other than that of the hand must be detected in the scene. These constraints will allow the precise determination of the shape and location of the hand by matching with already recorded models.

[0009] The main obstacle to this approach is the separation of the acquisition and processing devices. The information is processed pixel by pixel, resulting in a large amount of data to be processed. Segmentation requires very expensive mathematical calculations in terms of computing time. In addition, color classifications, segmentation and optimization are carried out in three different programs (algorithms).

[0010] The classical classifiers used by these systems, such as Hidden Markov Models and Neural Networks, require heavy preprocessing. Therefore, these systems cannot operate with low-cost, low-power equipment. They are relatively expensive and require a large number of gesture samples to converge. The available interfaces use cameras (CMOS, CDD) as well as a computer, bulky elements, which do not lend themselves to miniaturization.

[0011] Finally, we find the presence detection system. It is commonly used in sanitary elements, such as hand basins, faucets, urinals, showers, hand dryers, soap dispensers, etc. It is a control device allowing these devices to be triggered without requiring manual contact from the user. Such devices have the advantage of keeping the sanitary element clean and not touched by the user, which limits the development and transmission of germs or bacteria. Furthermore, they also make it possible to limit the quantity of water or electricity consumed by the sanitary element, since the latter only works in the presence of a user, and stops when the user moves away and no longer uses it.

[0012] It is therefore common to equip these sanitary elements with contactless devices, using infrared transmitters and sensors. The control device triggers the operation of the sanitary element according to the detected infrared intensity. If the intensity increases, this corresponds to the presence of a user near the detector. The example of such a device is described in particular in application FR 2 761 446 A1. There are, among other things, infrared detectors making it possible to detect the presence of a user without necessarily being mounted opposite a specific surface. These detectors are background suppression sensors, which, thanks to a system of lenses and a matrix of detectors, make it possible to estimate the distance separating them from the user more precisely than with standard infrared sensors.Background suppression sensors can therefore be mounted elsewhere than at the free end of the tap, such as at its base.. .

[0013] All of these devices operate automatically, resulting in a high rate of unwanted, untimely triggering.

[0014] One thing is clear: there is no solution capable of equipping conventional switches and giving them the ability to interact remotely with a human. Existing interaction devices are too heavy, power-hungry, and bulky to replace switches. Presence detectors operate automatically and do not interact with humans. The solution involves algorithmic and architectural adequacy. Description of the invention

[0015] A switch as symbolized in figure (1) below, is a device having at least two conductive cable paths (wire, bus) having terminals at the ends which are used to establish an electrical contact. Between these terminals? there is an activation device (or switch). This mechanism is covered with a surface on which an individual exerts manual physical pressure which establishes (or breaks) the connection. This operation, generally carried out by the finger of the hand, authorizes or interrupts the circulation of the electrical flow between the terminals.

[0016] To provide an answer to the problem of hygiene and safety

[0017] in public places equipped with this type of installation, the present invention proposes to replace the manually switched electrical (electronic) circuit with a remotely controlled electrical device. The new device would replace the manual switch ([Fig.2]) with an electrically activated switch (EAS).

[0018] The mechanical action of the finger is replaced as in figure (3) by a device (CAE), which then receives an electrical command and automatically modifies the configuration of the two terminals of the switch. The CAE device of the present invention is a basic electrical cell or an association of basic cells which receives a voltage on one side and lets the current pass on the other side. This device (see [Fig.4]) is in turn controlled by a second device, an image processing module (MTI: Image Processing Module).

[0019] The present invention proposes to use an image processing module to provide the voltage control of the CAE cell. This module (MTI) has a double functionality: to detect an object in the shape of a finger and that it is the color of a human's skin. Then, it must be able to implement skin color segmentation algorithms and at a useful rate. It is like the ALUs of DSPs, these processors are based on a general architecture ([Fig.5]) capable of carrying out the operations at the base of the information processing primitives. Finally, managed by a sequencer, and at the cost of a few storage elements, such an approach makes it possible to chain instructions into programs.

[0020] An image acquisition tool (see [Fig.6]) completes the entire device of the present invention. It is a network of standard resolution image sensors which can consist of a simple camera of the CCD (Charge Coupled Device), CMOS APS (Active Pixel Sensor) or other type. This device, interacting remotely with the environment, makes the physical and mechanical action visual. The images filmed continuously by this system are transferred to the image processing module which subsequently generates an electrical command which will activate (or not) the CAE switch.

[0021] The final device of the present invention proposes two architectures to address the problem of hygiene, safety, electrical consumption, computing power, and size: The first architecture is composed of an acquisition and processing module connected to a network of N CAEs and each itself associated with an auxiliary circuit, as illustrated in figure (7). One of these architectures uses a compact circuit of the VLSI (Very Large Signal Itegration) type, consisting of an image sensor matrix, an image processing device, a command-control interface, and possibly a radio frequency circuit depending on the interconnection mode. This compact circuit simultaneously performs image acquisition and processing on the one hand, as well as the control and command of the CAEs on the other hand. It will be produced in standard CMOS technology of low cost and low energy consumption.The processing device used is a DSP (Digital Signal Processing), used according to the operating mode of the present invention is capable of translating the skin color segmentation algorithms and tracking the movement of the finger in a scene. Once the image is processed, the MTI module decodes the information and transfers it via a wired connection, Wifi, or a local radio loop. A voltage command is provided to the corresponding CAE to activate or deactivate one of the ancillary circuits.

[0022] The second architecture is a balance on the one hand between electrical consumption, computing power, and hygiene and safety on the other. The present architecture is intended to replace the electrical diagram and wiring in the home. Thus the present invention proposes a single computer to carry out the processing of images coming from several cameras, and to have the computing power necessary to process several image sources at the same time and at a useful rate. Then to place cameras in place of switches so as to have a network of sensors whose only functions are to acquire and transmit images. (See [Fig.l 1]). For this the sensors are connected to the image processing module by a wired connection, or via a wireless transmission device.

[0023] The MTI is in turn connected to the CAEs, as previously, by a wired or wireless link. For each image received and processed by the MTI, an activation code for one (or more) CAEs is generated via the connections which will then activate the associated auxiliary circuits. Each CAE is interconnected to a control circuit and possibly equipped with a radio frequency circuit. This architectural organization makes it possible to increase the computing power of the MTI, to relocate the processing and possibly to reduce the cost of copper. Feasibility of an MTI.

[0024] One of the preferred algorithms of this invention is a combination of two approaches: The YCbCr chrominance space and the subtraction of the image from the background by temporal modeling of each pixel with a Gaussian mixture and this in an unsupervised manner. For segmentation, the algorithm used is derived from K-means. To simplify, the algorithm is described for a black and white image, it can easily be adapted for an RGB image: by replacing the standard deviation by a covariance matrix, and the intensity of pixel I by a vector of (R,G,B)......

[0025] Algorithm 1. Step 1 %Give a value to the temporal constancy 1 / a of the forgetting process, give a low value to the weight Q and a large value to the standard deviation o to force convergence, T bimodal parameter these %values ​​being derived from %1 learning.

[0026] a = aO; T=T0; Q=Q0 ; o=o0 ;

[0027] 2) Step 2

[0028] %Initialization of the parameters of the three Gaussians of the mixture of %each pixel, one Gaussian for the background image, one in case of %bimodal background image, one for the change of background %over time.

[0029] For each Gaussian of each pixel do

[0030] Fork=l:3

[0031] Q(k,t=0)=0 Q(k,t=0)=0;

[0032] o(k,t=0)=o0 o(k,t=0)=oO;

[0033] p(k,t=O)=O p(k,t=O)=O;

[0034] end for

[0035] %o is the variance, Q is the weight, p is the mean, t is the %time.

[0036] End For each Gaussian of each pixel do

[0037] 3) Step 3

[0038] % Calculation of the Gaussian parameters for the current image at %1'time t

[0039] For all pixels of intensity I(t) at time t

[0040] For each pixel do

[0041] Fork=l:3

[0042] Find k which minimizes this function;

[0043] abs(p(k,t)-I(t)) / o(k,t)abs(pk,t-It) / o(k,t);

[0044] k=k0;

[0045] End for

[0046] if(abs(p(kO,t-I(t))<2.5*o(kO,t)abspkO,t-It<2.5*o(kO,t))

[0047] %Mettre à jour des paramètres associés à cette gaussienne.

[0048] p(k0,t)=(l-a)p(k0,t-l)+aI(t); pkO,t=l-opkO,t-l+oI(t) ;

[0049] o2(kO,t)=(l-a)*o2(kO,t-l)+a*(I(t)-p(kO,t))2;

[0050] o2k(0,t)=l-a*o2k0,t-l+a*It-pk0,t2;

[0051] Q(k0,t)=(l-a)Q(k0,t-l)+a Qk0,t=l-aQk0,t-l+a ;

[0052] If (k^kO)

[0053] Q(k^k0,t)=( 1 -a)Q(k0,t-1 );Qk^k0,t= 1 -aQk0,t-1 ;

[0054] End if

[0055] QN=Q(l,t)+Q(2,t)+Q(3,t) ;

[0056] For i=l :3 %normalisation des Q

[0057] Q(i,t)=Q(i,t) / QN;

[0058] End for

[0059] % l’image de référence du pixel

[0060] Iref=p(kO,t) ;

[0061] Else

[0062] For i =1:3

[0063] Calculate the largest ratio Q(i,t) / o(i,t)

[0064] i0=i ;

[0065] End for

[0066] % initialize a new Gaussian

[0067] p(iO,t)=I(t)p(iO,t)=I(t);

[0068] o(iO,t)=oOo(iO,t)=oO;

[0069] Q(i0,t)=Q0;Q(i0,t)=Q0;

[0070] %the future reference image

[0071] Iref=p(i0,t);

[0072] End else

[0073] End if

[0074] Image = I(t)-Iref(t-1)

[0075] % subtraction of the current image with the background image

[0076] If ( CrO <Cr<Crl et CbO<Cb<Cbl)

[0077] %YCrCb chroma space

[0078] So Skin color image;

[0079] End

[0080] End For all pixels of intensity I(t) at time t,

[0081] 4) Step 4

[0082] %Detection of the direction and the direction of variation of the finger on the scene.

[0083] Detected if the finger is present on the scene;

[0084] Detector the sense and direction of finger movement: ;

[0085] case 0: no movement

[0086] case 1: from right to left

[0087] case 2: from left to right

[0088] case 3: from bottom to top

[0089] case 4: from top to bottom

[0090] case 5: diagonal from bottom left to top right

[0091] case 6: diagonal from high right to low left

[0092] case 7: diagonal from top left to bottom right

[0093] case 8: diagonal from bottom right to top left

[0094] Analysis of the algorithm.

[0095] The labeling algorithm is iterative, each iteration is broken down into 3 phases: • A labeling phase, • A phase of updating the cluster parameters (p, o and Q), • A normalization phase. At the end of this phase, the geometric characteristics of the 3 clusters are extracted (barycenter, surface, length, width).

[0096] Iterative phase of segmentation:

[0097] The pixels are labeled into k=3 categories. The category assignment of each pixel is performed based on the distance to the target value (p) of the category.

[0098] Once the pixels are labeled, the parameters p, o and Q of each of the k=3 categories are updated according to a recursive average if the distance from the target value to the gray level (or chrominance) value is less than an amount proportional to the o of the cluster according to the following principle: IF(

[0099] ENDIF

[0100] Post segmentation phase:

[0101] The geometric characteristics (length, width, barycenter, surface area, etc.) of each of the clusters are calculated.

[0102] From this data, the calculation of the position of fingers is updated. The number of elementary operations required is substantially equal to 2-3 times the number of pixels.

[0103] Evaluation of the number and types of operations required

[0104] Assumptions: The target architecture is given below:

[0105] The operations are performed at the bottom of the column by a dedicated SIMD (Single Instruction Multiple Data) type processor vector. The instruction set of the elementary processor is optimized to perform the calculations.

[0106] It will be assumed that the divisions can be performed in 1 operation.

[0107] We will assume that the convergence of the algorithm is reached after 8 iterations.

[0108] We will assume that the imager is monochrome and uses one of the formats below: below:

[0109] [tab 1] RxC RC Imager CGA 200 320 QVGA 240 320 VGA 480 640 Table 1 Standard formats of low-cost imagers

[0110] A monochrome imager can take up a format such as this.

[0111] We will assume operation at a rate of 50 or 60 images / s to avoid beat phenomena with artificial lighting.

[0112] Results: We then arrive at the table below.

[0113] [Tab 2] type of operation number of pixels number of dus ter number of ops degree of parallelization possible Labeling subtraction / pixel / cluster RxC k RxCxk C division / pixel / cluster RxC k RxCxk C comparison / pixel / cluster RxC k RxCxk C assignment / pixel / cluster RxC k RxCxk C storage / pixel / cluster RxC k RxCxk Update comparison / pixel / cluster RxC k RxCxk 2 multiplications x 3 by ameters (p, sigma, Orne ga) RxCx2 x3 k RxCxk 1 addition 1 division / cluster Other Length / width conditional increment / pixel / cluster RxC k RxCxk C Area conditional increment / pixel / cluster RxC k RxCxk C Barycenter Multiplication / accumulation RxC k RxCxk C Table 2: Census of the number of operations

[0114] Operating at a frame rate of 50 or 60 frames / s we could achieve results like these.

[0115] For a CGA format and a frame rate of 50fps, we arrive at a total of 500 MOps, or a total of 2M instructions / s with the target architecture.

[0116] For a VGA format and a frame rate of 50fps, we arrive at a total of 2 Gops, a total of 4M instructions / s with the target architecture.

[0117] Use of an IcyCam® type solution

[0118] Reminder of the architecture The architecture of the IcyCam is given below

[0119] The main characteristics are: • 32-bit DSP / MCU icyflexl trial output • 64-bit data bus • Contrast and Orientation computation in the data readout path • Graphical Processing Unit (GPU) • 32-bit 100 MHz SDRAM interface • 12-bit Parallel Peripheral Interface (PPI) • 2 Serial Peripheral Interfaces (SPI) • 16-bit General Purpose I / O Interface (GPIO) • UART interface • JT AG interface • Programmable in C (GNU tool suite) • Max. System clock rate: 33 MHz

[0120] Analysis of processing capabilities

[0121] Considering the architecture, we can estimate the maximum computing capacity at 50MIPS. The IcyCam allows processing at a safe video rate for simple algorithms (optimal thresholding, contour detection by linear filtering and thresholding, etc.). On the other hand, for a k-means type segmentation algorithm, this architecture is limited.

[0122] To overcome this limitation, the target architecture must allow a decimation of the total number of pixels and an increase in the degree of parallelism of the photosensitive matrix.

[0123] The image processing circuit of the present invention uses the principle of a target architecture. An architecture which brings together in a single ASIC circuit a photo sensor matrix, a vector of column processors, storage elements, and a microcontroller. Then, to increase the degree of parallelism, the target architecture integrates around each sensor, the skin color image segmentation function in the chrominance space (R, G, B). This equation is described below:

[0124] (R > 95) AND (G > 40) AND (B > 20) AND (MAX(R, G, B) - MIN(R, G, B) > 15) AND (ABS(RG) > 15) AND (R > G) AND (R > B). This amounts after simplification to: (R > 95)&(G > 40)&(B > 20) &(R > G+15)&(R>B).

[0125] Then the algorithm must manage the reduction of the decimation of the pixel matrix: 1) by reducing the number of columns by any factor X, 2) by dividing the number of images processed per second by a factor Y. That is a total decimation by a factor XY of the total number of pixels. Description of the embodiments

[0126] An example of realization according to the architectural model: The developed electrical diagram of a double ignition installation in a home. The two switches are replaced by two finger movements to be performed in front of a camera. The gestures are to be performed between 10 cm and 2.5 m from the front of the camera and following an angle (+-) 60 degrees from the perpendicular to the lens. One movement to turn on and another to turn off, i.e. four movements in total as illustrated in figure 20 to achieve the double ignition.

[0127] Example of Realization of a CAE: One of the embodiments of the CAE is illustrated in the diagram of [Fig.13], 14 it is an electrical relay controlled by a voltage. Activation is done by providing a voltage peak Vd on terminals c and d, the switch goes from L1 to N or vice versa.

[0128] Example of the Production of an MTI

[0129] A possible embodiment of an MTI of the present invention from commercial electronic elements, uses a standard computer equipped with a camera, a USB port, a parallel port, a serial port. By “standard” is meant a PC where the editing and communication software is installed, as well as the various peripheral drivers. In this PC is installed the image processing software, on its USB port is connected a 5Volt / 230Volts electrical relay, and on the latter is interconnected the phase and the “phase return”, on one side the phase comes from the electrical network of a home and the “phase return” is connected to a 220Volts lamp as well as the associated neutral. And using Matlab software the target image processing algorithm was implemented.This is a program that detects the variation of a hand's finger in four directions: bottom to top, top to bottom, diagonally from bottom to top, and diagonally from top to bottom. This corresponds to four control actions, respectively, turning on, turning off, increasing, and decreasing the light. In another embodiment, the variations of the above movement can be reversed, resulting in 8 actions in total.

[0130] And if we add the finger pointing gesture to these 8 variations, we obtain an 8 to 9 digit “digicode”.

[0131] ILLUSTRATIVE DIAGRAMS OBTAINED BY MATLAB SIMULATION

[0132] [Fig. 15] illustrates the part of the algorithm dealing with the case of the hand in an ascending phase. The hand would have to be in an ascending phase for at least 10 successive images and would have to reach the top of the screen for the increase in the intensity of the lamp to be triggered.

[0133] [Fig. 16] illustrates the part of the algorithm dealing with the case of the hand in a descending phase. The hand would have to be in the descending phase for at least 10 images successively and that it reaches the bottom of the screen so that the reduction in the intensity of the lamp is triggered.

[0134] [Fig. 17] illustrates the part of the algorithm dealing with the case of the hand in a diagonal upward movement phase. The hand would have to be in a diagonal upward movement phase for at least 10 successive images and would have to arrive towards the extreme left of the top of the screen for the lamp to be switched on.

[0135] [Fig. 18] illustrates the part of the algorithm dealing with the case of the hand in a downward diagonal movement phase. The hand would have to be in a downward diagonal movement phase for at least 10 successive images and would have to arrive towards the extreme right of the bottom of the screen for the lamp to be switched off. Brief description of the drawings

[0136] [Fig.l]:

[0137] This figure represents the symbols of switches open, another closed. The two vertical half-lines symbolize the action of opening and closing. And the middle part the switch or presses to turn on or off a lamp.

[0138] [Fig.2]:

[0139] This figure represents a common manual switch with its switch in the center which allows switching from one mode to another.

[0140] [Fig.3]:

[0141] This figure symbolizes a system capable of acting remotely to open or close a switch. The device receives a voltage and acts in place of human mechanical action to interconnect the 2 electrical wires.

[0142] [Fig.4]:

[0143] This figure represents an auto electric switch (AES) connected to the Image Processing Module (IPM) which provides a VD control signal.

[0144] [Fig.5]:

[0145] This figure represents the general architecture of the image processing device with the essential elements. It includes processors, a pixel plane, a microcontroller, a communication bus interface, and the mixed processor lines.

[0146] [Fig.6]:

[0147] This figure represents the CAE interconnected to the MTI by a wired link. The MTI is connected to the image acquisition device. This sends the images to the MTI which processes them and provides command and control of the CAE, which will activate or not the electrical flow.

[0148] [Fig.7]:

[0149] This figure represents the overall device of the invention according to the embodiment of architecture 1. It shows the assembly between the camera with the image processing module and the auto electric switch in a single electric circuit.

[0150] A compact printed circuit where the image acquisition device and the MTI will be made of low-cost Very Large Signal Integrated (VLSI) CMOS ASIC.

[0151] [Fig.8]:

[0152] This is the target architecture of the image processing device of the invention. This architecture is derived from the target algorithm of the invention. It is a matrix of photo sensors, processor columns, temporary storage elements and global memory planes.

[0153] [Fig.9]:

[0154] Reminder of the standard Icycam type image processing architecture. It is an architectural solution that uses memory RAM and a 320*240 QVGA camera associated with an Icyflex type processor.

[0155] [Fig. 10]:

[0156] This figure represents an Icyflex type image processing processor architecture with memory interfaces and logical calculation units.

[0157] [Fig. 11]:

[0158] This figure shows all the devices of the invention in an electrical diagram of the dwelling and according to the embodiment of architecture 2.

[0159] [Fig. 12]:

[0160] This figure represents the use of the device of the invention within the electrical installation of a home for double ignition. An individual can communicate with the device from a distance of 20 cm to 2 m.

[0161] [Fig.13]:

[0162] This figure represents an example of CAE. It is an open or closed electrical relay controlled by voltage VD.

[0163] [Fig. 14]:

[0164] This figure represents a lamp connected to a relay with a voltage of 5 volts. This is an example of a CAE implementation with a 5 / 230V relay.

[0165] [Fig. 15]:

[0166] This figure represents the result of the target algorithm implemented in a PC which detects the movement of the finger of the hand from bottom to top.

[0167] [Fig. 16]:

[0168] This figure represents the result of the target algorithm implemented in a PC which tracks the movement of the finger from top to bottom.

[0169] [Fig. 17]:

[0170] This figure represents the result of the target algorithm implemented in a PC which tracks the movement of the finger from the low right diagonal to the high left.

[0171] [Fig.18]:

[0172] This figure represents the result of the target algorithm implemented in a PC which tracks the movement of the finger from the top right diagonal to the bottom left.

[0173] [Fig. 19]: The four directions of movement for switching on and off two lamps

Claims

Claims

1. Switch device in which an electrically activated switch (CAE) receives an electrical command and automatically modifies the configuration of the two terminals of the switch, the switch (CAE) being associated with an acquisition tool device, then with an image processing module device, and the assembly being produced in the same compact circuit, with low energy consumption and of the VLSI (Very Large Signal Integration) type, said compact circuit simultaneously carrying out the acquisition and image processing on the one hand, as well as the control and command of said electrically activated switch.

Images