Image Segmentation Using Depth Clustering and Cascade Classification
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Solution Overview
Problem
Current image segmentation technologies face challenges in accurately separating objects, particularly humans, from backgrounds in unconstrained indoor environments with varying lighting conditions, low contrast, and non-homogeneous colors, often resulting in incomplete depth data and high computational demands.
Innovation Solution
The system employs a combination of depth sensors and color cameras to generate aligned depth and RGB images, using a cascade approach with classifiers and priors to refine segmentation, filling depth holes, and leveraging training data for accurate blob selection and classification, even with limited processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image segmentation methods are used in unconstrained indoor environments, then the system can process images with varying lighting conditions, but the segmentation accuracy deteriorates due to low contrast and non-homogeneous colors
Solution Approach 1:
The patent transitions from 2D color image analysis to 3D depth-aware segmentation by incorporating depth maps and spatial distance information. This dimensional enhancement allows the system to segment objects based on their spatial relationships and depth characteristics rather than relying solely on color and texture cues that fail in challenging lighting conditions.
Solution Approach 2:
The patent divides the segmentation process into multiple stages: initial blob detection, depth-based filtering, and refined classification. This multi-stage segmentation approach allows the system to progressively refine segmentation accuracy by applying different criteria at each stage, overcoming the limitations of single-method approaches in varying lighting conditions.
2Measurement precision
If complex segmentation algorithms are employed to improve accuracy, then segmentation precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by using simplified blob detection for initial segmentation and reserving more computationally intensive classification methods for only those regions that require refined segmentation. This selective application of computational resources maintains accuracy while reducing overall complexity.
Solution Approach 2:
The patent performs preliminary depth map generation and blob detection before applying more complex classification algorithms. This preliminary action filters out easily segmentable regions and prepares data structures that facilitate faster processing in subsequent stages, reducing overall computational burden.
3Measurement precision
If depth data is used to enhance segmentation accuracy, then measurement precision improves, but data completeness deteriorates due to depth holes and incomplete depth information
Solution Approach 1:
The patent introduces color image data and spatial relationship information as intermediary elements that bridge the gaps caused by depth holes. By combining depth information where available with color and spatial cues from the color image, the system maintains segmentation accuracy without being hindered by incomplete depth data.
Solution Approach 2:
The patent dynamically adjusts the reliance on depth versus color information based on data quality. In regions with complete depth information, depth-based segmentation is prioritized for accuracy. In regions with depth holes, the system transitions to color and spatial parameter-based segmentation, maintaining overall data completeness and accuracy.
Data Source
AI summary
Devices and techniques are generally described for segmentation of image data using depth data. In various examples, color image data may be received from a digital camera. In some examples, depth image data may be received from a depth sensor. In various examples, the depth image data may be separated into a plurality of clusters of depth image data, wherein each cluster is associated with a respective range of depth values. In some further examples, a determination may be made that a first cluster of image data corresponds to an object of interest, such as a human subject, in the image data. In various examples, pixels of the first cluster may be encoded with foreground indicator data. In some further examples, segmented image data may be generated. The segmented image data may comprise pixels encoded with the foreground indicator data.