354 results about "Lab color space" patented technology

Color information of a color image is represented by the Lab color space. A user can change hue by moving the position of a reference circle displayed on the a*b* plane in the Lab color space and change saturation by changing the size of the reference circle. The color information of the color image is changed with changes in the reference circle, allowing the user to conjecture changes in the color image visually and make color corrections easily and quickly.

The invention relates to a low-quality image enhancement method under extreme weather conditions. According to the method, as for an inputted single image, the image is converted to a CIE-Lab color space; a color cast factor D is set; according to experience, if D is smaller than 1.4, the image is a clear image which does not require processing; if D is larger than 1.4, the image is a degraded image; whether the image is a dust image or a haze, rain and snow image is judged according to chrominance component values; if the image is in a haze, rain and snow image, an improved dark primary color priori algorithm is adopted to process the image; if the image is a dust image, a gamma-corrected contrast limited adaptive histogram equalization algorithm is adopted. With the method adopted, problems in the prior art can be well solved. The effects of the method are greatly improved compared with the in the prior art, and the popularization and application of the method can be benefitted.

If gamut mapping (hue restoration) defined by one Lab color space is applied in color matching under different reference white points, the human vision perceives the hue as inconsistent. In view of this, input data which is dependent on a color space of an input device is converted by the conversion LUT 11 to color space data which is independent of any devices, based on a viewing condition at the time of viewing an input original. The data is converted to data in the human color perception space by the forward converter 12, then subjected to gamut mapping, and converted back to data in the color space independent of any devices by the inverse converter 15, based on a viewing condition at the time of viewing an output original. Then, the data is converted to output data in a color space which is dependent on an output device by the conversion LUT 16.

If gamut mapping (hue restoration) defined by one Lab color space is applied in color matching under different reference white points, the human vision perceives the hue as inconsistent. In view of this, input data which is dependent on a color space of an input device is converted by the conversion LUT 11 to color space data which is independent of any devices, based on a viewing condition at the time of viewing an input original. The data is converted to data in the human color perception space by the forward converter 12, then subjected to gamut mapping, and converted back to data in the color space independent of any devices by the inverse converter 15, based on a viewing condition at the time of viewing an output original. Then, the data is converted to output data in a color space which is dependent on an output device by the conversion LUT 16.

The invention discloses an image type defect detecting method for an insulator. The image type defect detecting method for the insulator comprises the steps that firstly, the insulator is preliminarily separated from a complicated background image in a Lab color space by the adoption of the K mean value clustering algorithm according to the characteristic that the color of the insulator has a clustering property in a colored image; then, the specific location of the insulator is positioned by the adoption of the connected region judgment method based on principal component analysis according to the strip symmetry characteristic, formed in the image, of the insulator; on this basis, a defect area of the insulator is recognized by conducting statistical analysis on the distance between every two adjacent discs of the insulator. The image type defect detecting method for the insulator has the advantages that the influence of different kinds of light and the complicated background to the method is small, the accuracy and robustness of insulator defect detection are improved, and the engineering practical value is quite high.

The invention provides a quality estimation method of exit-entry digital portrait photos. The quality estimation method comprises the following steps: the format size and file size detection of each digital portrait photo, face detection, eye positioning, face horizontal centering detection, the exposure detection of global luminance deviation and partial luminance deviation, the clearness detection of contrast evaluation and ambiguity evaluation and the color detection and the background detection for detecting the skin naturalness in an Lab color space and judging whether colors in an image face area are qualified or not. The quality estimation method has the advantages that a quality estimation module of the digital portrait photos, specific quality detection items, detection flows, estimation indexes and estimation methods are established, which is different from the prior quality estimation method of the digital portrait photos relying on subjective estimation. The invention judges the quality of each digital portrait photo without relying on a standard image, obtains the judging result in accordance with the subjective feeling of people and the requirements of a face recognition system, thereby favorably satisfying the business requirements for managing the exit-entry digital portrait photos.

If one gamut mapping (hue restoration) defined by the Lab color space is applied in color matching under different reference white points, the human vision perceives the hue as inconsistent. In view of this, input data which is dependent on a color space of an input device is converted by the conversion LUT 11 to color space data which is independent of any devices, based on a viewing condition at the time of viewing an input original. The data is converted to data in the human color perception space by the forward converter 12, then subjected to gamut mapping, and converted back to data in the color space independent of any devices by the inverse converter 15, based on a viewing condition at the time of viewing an output original. Then, the data is converted to output data in a color space which is dependent on an output device by the conversion LUT 16.

A method and system for segmenting an object in a digital image are disclosed. A user selects at least one foreground pixel or node located within the object and at least one background pixel or node located outside of the object. A random walk algorithm is performed to determine the boundaries of the object in the image. In a first step of the algorithm, a plurality of coefficients is determined. Next, a system of linear equations that include the plurality of coefficients are solved to determine a boundary of the object. The processing is performed by a graphics processing unit. The processing can be performed using the near-Euclidean LUV color space or a Lab color space. It is also preferred to use a Z-buffer in the graphics processing unit during processing. The object, once identified, can be further processed, for example, by being extracted from the image based on the determined boundary.

The invention discloses a remote sensing image region of interest detection method based on integer wavelets and visual features, which belongs to the technical field of remote sensing image target identification. The implementing process of the method comprises the following steps: 1, performing color synthesis and filtering and noise reduction preprocessing on a remote sensing image; 2, converting the preprocessed RGB spatial remote sensing image into a CIE Lab color space to obtain a brightness and color feature map, and converting an L component by using integer wavelets to obtain a direction feature map; 3, constructing a Gaussian difference filter for simulating the retina receptive field of a human eye, performing cross-scale combination in combination with a Gaussian pyramid to obtain a brightness and color feature saliency map, and performing wavelet coefficient sieving and cross-scale combination to obtain a direction feature saliency map; 4, synthesizing a main saliency map by using a feature competitive strategy; and 5, partitioning the threshold values of the main saliency map to obtain a region of interest. Due to the adoption of the remote sensing image region of interest detection method, the detection accuracy of a remote sensing image region of interest is increased, and the computation complexity is lowered; and the remote sensing image region of interest detection method can be applied to the fields of environmental monitoring, urban planning, forestry investigation and the like.

The invention provides a color histogram based vehicle body color identification method. The method comprises three main steps of license plate positioning, vehicle body color feature region positioning and vehicle body color identification. The method particularly comprises: firstly, determining a license plate position through a license plate identification technology, and detecting a coarse region of vehicle body color through the height, width and position coordinate information of a license plate; preprocessing the region of the vehicle body color to reduce external environment interferences; further searching the preprocessed coarse region of the vehicle body color to obtain an accurate feature region of the vehicle body color; and converting the feature region into a Lab color space, extracting a color histogram from the Lab space, and performing vehicle body color training and identification by using a nonlinear SVM. The method is capable of intelligently processing traffic vehicle videos and images and automatically identifying the vehicle body color. Compared with other schemes in the same field, the method has very high identification accuracy and is high in robustness in a complicated environment.

A CIE Lab color space based gray threshold segmentation method comprises the following steps of 1 transforming an image of a RGB color space into a CIE Lab color space, 2 conducting Gaussian histogram filtering on all gray channels of the CIE Lab color space, 3 adopting an Otsu threshold value method to calculate threshold values of the gray channels and adjusting the threshold values as local minimum, 4 calculating gray separation degrees of the gray channels of the CIE Lab color space, selecting the gray channel with highest gray separation degree and adopting the corresponding threshold value calculated in the step 3 to perform binaryzation division. By means of the CIE Lab color space based gray threshold segmentation method, the problem of division of a part of color images is well solved. In addition, the division operation can serve as pre-division operation conducted on complicated color images, and proposed separation degree index can also serve as a standard for image estimation.

The invention, which relates to the digital image processing field, provides a visual-saliency-based random image region quality evaluation strategy, so that an evaluation result meets the subjective perception of the human being. According to the technical scheme, the method comprises: (1), Gaussian filtering is carried out on a reference image IR and a distorted image ID respectively and the filtered image RGBs are converted into Lab color space units; (2), for different image sizes r*c, N windows with random sizes are generated randomly; (3), visual saliency calculation is carried out the reference image and the distorted image respectively by using a visual-saliency extraction algorithm and a visual saliency similarity matrix in each random window is obtained; (4), weighting and integration are carried out on the obtained similarity matrixes of all random windows. The method is mainly applied to an image processing occasion.

The present invention discloses a remote sensing image region-of-interest detection method based on multi-significant-feature fusion, belonging to the technical fields of remote sensing image processing and image identification. The remote sensing image region-of-interest detection method comprises the following steps: 1) obtaining color channels of one group of input remote sensing images and calculating a color histogram of each color channel; 2) calculating a standard significant weight of each color channel according to the color histograms; 3) calculating an information content significant feature image; 4) converting one group of input remote sensing images from an RGB color space to a CIE Lab color space; 5) utilizing a clustering algorithm to obtain clusters; 6) calculating a significant value of each cluster, and obtaining a common significant feature image; 7) fusing the information content significant feature image with the common significant feature image to obtain a final significant image; and 8) performing threshold segmentation through an OTSU method to extract a region of interest. Compared with a traditional method, the remote sensing image region-of-interest detection method of the present invention achieves accurate detection for a remote sensing image region-of-interest on the premise of not having a prior knowledge base, thus the remote sensing image region-of-interest detection method can be widely applied to fields such as environment monitoring, land utilization and agricultural investigation.

The invention belongs to the technical field of image processing and discloses a method and an alarm system for automatically identifying the wearing state of a work clothes work cap based on a video.Based on a video stream time sequence image, a moving object in the video image is extracted by combining a time domain difference and background subtraction. Classification of human objects and other objects is accomplished according to the detected feature of the target block and the time period feature of the outline of the block. Segmentation of the head and torso of the detected human target; In Lab color space, whether the human target head wears safety helmet or not and whether the torso part wears working clothes are analyzed intelligently. If security protection abnormality is found,the security alarm message shall be reported to the manager in real time, and at the same time, the security alarm message shall be filmed and archived. The invention does not need to increase the installation and maintenance cost of the equipment. Adopting artificial intelligence technology, it is easy to use and low cost to realize the all-weather monitoring of workers' and employees' service caps on the construction site, which has a great application prospect.

The invention discloses a method and device for embedding and extracting digital watermarking. The method comprises the steps of: conducting discrete cosine transform (DCT) to a low-frequency area obtained by generating a CDMA (Code Division Multiple Access) watermarking sequence and carrying out discrete wavelet transform (DWT) to a host image; embedding the CDMA watermarking sequence into a medium-high frequency DCT coefficient sequence determined by a secret key. No color loss can be ensured in color mode translation by embedding watermarking in the LAB color space of the image, the robustness and invisibility of the watermarking can be improved by adding watermarking onto the DWT and DCT transformed coefficient of the image, and the capacity of the watermarking can be increased through the CDMA coding technique.

The invention discloses a method for dynamically adding subtitles based on the video content. The method comprises the following steps of: converting a video image to be processed from RGB color space into a CIE-Lab color space; extracting time axis information and text bitmaps of subtitles to be added; searching for an addition region of each piece of subtitle text, namely searching for the addition region of each piece of subtitle text by calculating the sum of energy values of all pixels in regions with the same resolution as that of the text bitmap of the subtitle text; and finally adding the subtitle texts into the corresponding searched regions to finish the operation of adding the subtitles of the video image. Each step of the method of the invention can be processed on a home computer, so the problem that the conventional subtitle addition method only simply adds the subtitle texts into the fixed regions of the image in ignorance of the possible damage to the image content is effectively solved, and a simple and intuitive method for dynamically adding the subtitles based on the video content is provided for the non-professional personnel.

If gamut mapping (hue restoration) defined by one Lab color space is applied in color matching under different reference white points, the human vision perceives the hue as inconsistent. In view of this, input data which is dependent on a color space of an input device is converted by the conversion LUT 11 to color space data which is independent of any devices, based on a viewing condition at the time of viewing an input original. The data is converted to data in the human color perception space by the forward converter 12, then subjected to gamut mapping, and converted back to data in the color space independent of any devices by the inverse converter 15, based on a viewing condition at the time of viewing an output original. Then, the data is converted to output data in a color space which is dependent on an output device by the conversion LUT 16.

The invention discloses a method for detecting boll opening of cotton based on image detection. The method specifically comprises two stages, namely a training stage for counting the change rule of a cotton region in a cotton field image in a Lab color space through history image data, and a detection stage for detecting a real-time cotton field video image by using the change rule of cotton counted in the training stage in the Lab color space. The method comprises the following specific steps of: (1) detecting a single cotton image, detecting a candidate cotton region in the image according to a counting result and generating a binary result image; (2) marking and de-noising a communication domain, marking the binary result image and removing small noise interference by setting an area threshold value of the communication domain; (3) making a comprehensive judgment, voting each candidate region by using detection results at different moments of each day, selecting a stably existing region serving as the final cotton region detection result and further removing the interference of random noise; and (4) outputting a result image. By adopting the method, the influence of illumination is effectively overcome by using the Lab color characteristic of cotton and the continuity of images on the same day, and the presence of cotton in a cotton field image is detected correctly.

A lamp having improved color quality scale is provided. The lamp has a light-transmissive envelope and a phosphor layer comprising a first phosphor and a second phosphor wherein the first phosphor has an emission band with a maximum between 590 nm and 640 nm and the second phosphor has an emission band with a maximum between 520 nm and 570 nm. The light generated by the phosphor layer, when the lamp is energized, has delta chroma values for all fifteen color samples of the color quality scale within select parameters. The delta chroma values are measured in the CIE LAB color space.

The invention discloses an intelligent steel slag detection method and system based on a convolutional neural network. The method comprises the steps of steel slag image recognition, steel flow targetdetection and color steel slag image segmentation, wherein a color steel slag image in a video frame image serves as an object, and the color steel slag image is recognized through an image recognition method based on an improved AlexNet convolutional neural network; the method comprises the following steps: detecting steel flow information in a color steel slag image, and detecting a steel flowfrom a complex background through a target detection method based on a YOLOv3 convolutional neural network, thereby accurately detecting the slag inclusion condition of the steel flow; a color image is preprocessed based on a K-means clustering algorithm of a Lab color space, and steel slag is completely separated from molten steel by adopting an improved Otsu image segmentation algorithm. And carrying out steel slag visual detection by utilizing a visual user interface system. The method is simple and easy to implement and low in cost, steel slag and molten steel can be distinguished, false detection is avoided, the real-time recognition precision of the steel slag image is improved, and the purity of the molten steel is improved.

The invention relates to the field of computer image high-dynamic-range imaging technology, and provides an exposure fusion method in consideration of brightness distribution and detail presentation. The method comprises the steps of decomposing a low-dynamic-range multiple-exposure-image sequence into a Lab color space, and obtaining a brightness sequence and a color sequence; calculating a brightness global weight function according to the brightness sequence; segmenting the brightness sequence on a line number and row number plane for obtaining a plurality of sub-pixel calculating units, and obtaining a brightness detail weight function, segmenting the color sequence on the line number and row number plane for obtaining a plurality of sub-pixel calculating units, and obtaining a color detail weight function; performing weighted summation on the brightness sequence by means of the brightness global weight function and the brightness detail weight function; and performing weighted summation on the color sequence by means of the color detail weight function for obtaining a fused color component. The exposure fusion method realizes single high-dynamic-range quick imaging from a plurality of low-dynamic-range images and furthermore satisfies a requirement for brightness distribution and detail presentation.