New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.

Stereo correspondence using an assisted discrete cosine transform method.

Many approaches have been taken towards the development of a compliant stereo correspondence algorithm that is capable of producing accurate disparity maps within a short period of time. There has been great progress over the past decade due to the vast increase in optimization techniques. Currently, the most successful algorithms contain explicit assumptions of the real world such as definitive differences in disparity among objects and constant textures within objects. This thesis starts by giving a brief description of disparity, along with descriptions of some common applications. Next, it explores various methods used in common stereo correspondence algorithms, as well as gives an in depth description and analysis of top performing algorithms. These algorithms are later used to compare with the proposed algorithm. In the proposed algorithm, frequency stereo correspondence in parallel with the traditional color intensity stereo correspondence is used to develop an initial disparity map. Frequency stereo correspondence is achieved using a winner-take-all block based Discrete Cosine Transform (DCT) to find the largest frequency components as well as their positions to use in disparity estimation. The proposed algorithm uses methods that are computationally inexpensive to reduce the computational time that plagues many of the common stereo correspondence algorithms. The proposed algorithm achieves an average correct disparity rate of 95.3%. This results in a disparity error rate of 4.07% compared to the top performing algorithms in the Middlebury website [1]; the DoubleBP, CoopRegion, AdaptingBP, and ADCensus algorithms that have error rates of 4.19%, 4.41%, 4.23%, and 3.97%, respectively. Additionally, experimental results demonstrate that the proposed algorithm is computationally efficient and significantly reduces the processing time that plagues many of the common stereo correspondence algorithms.

Stereo correspondence using an assisted discrete cosine transform method.

Many approaches have been taken towards the development of a compliant stereo correspondence algorithm that is capable of producing accurate disparity maps within a short period of time. There has been great progress over the past decade due to the vast increase in optimization techniques. Currently, the most successful algorithms contain explicit assumptions of the real world such as definitive differences in disparity among objects and constant textures within objects. This thesis starts by giving a brief description of disparity, along with descriptions of some common applications. Next, it explores various methods used in common stereo correspondence algorithms, as well as gives an in depth description and analysis of top performing algorithms. These algorithms are later used to compare with the proposed algorithm. In the proposed algorithm, frequency stereo correspondence in parallel with the traditional color intensity stereo correspondence is used to develop an initial disparity map. Frequency stereo correspondence is achieved using a winner-take-all block based Discrete Cosine Transform (DCT) to find the largest frequency components as well as their positions to use in disparity estimation. The proposed algorithm uses methods that are computationally inexpensive to reduce the computational time that plagues many of the common stereo correspondence algorithms. The proposed algorithm achieves an average correct disparity rate of 95.3%. This results in a disparity error rate of 4.07% compared to the top performing algorithms in the Middlebury website [1]; the DoubleBP, CoopRegion, AdaptingBP, and ADCensus algorithms that have error rates of 4.19%, 4.41%, 4.23%, and 3.97%, respectively. Additionally, experimental results demonstrate that the proposed algorithm is computationally efficient and significantly reduces the processing time that plagues many of the common stereo correspondence algorithms.

Jupiter's Zonal Vorticity Profile Observed by JunoCam

<p>We derive Jupiter's zonal vorticity profile from JunoCam images, with Juno's polar orbit allowing the observation of latitudes that are difficult to observe from Earth or from equatorial flybys.  We identify cyclonic local vorticity maxima near 77.9°, 65.6°, 59.3°, 50.9°, 42.4°, and 34.3°S planetocentric at a resolution of ~1°, based on analyzing selected JunoCam image pairs taken during the 16 Juno perijove flybys 15-30. We identify zonal anticyclonic local vorticity maxima near 80.7°, 73.8°, 62.1°, 56.4°, 46.9°, 38.0°, and 30.7°S.  These results agree with the known zonal wind profile below 64°S, and reveal novel structure further south, including a prominent cyclonic band centered near 66°S. The anticyclonic vorticity maximum near 73.8°S represents a broad and skewed fluctuating anticyclonic band between ~69.0° and ~76.5°S, and is hence poorly defined. This band may even split temporarily into two or three bands.  The cyclonic vorticity maximum near 77.9°S appears to be fairly stable during these flybys, probably representing irregular cyclonic structures in the region. The area between ~82° and 90°S is relatively small and close to the terminator, resulting in poor statistics, but generally shows a strongly cyclonic mean vorticity, representing the well-known circumpolar cyclone cluster.</p><p>The latitude range between ~30°S and ~85°S was particularly well observed, allowing observation periods lasting several hours. For each considered perijove we selected a pair of images separated by about 30 - 60 minutes. We derived high-passed and contrast-normalized south polar equidistant azimuthal maps of Jupiter's cloud tops. They were used to derive maps of local rotation at a resolution of ~1° latitude by stereo-corresponding Monte-Carlo-distributed and Gauss-weighted round tiles for each image pair considered. Only the rotation portion of the stereo correspondence between tiles was used to sample the vorticity maps. For each image pair, we rendered ~40 vorticity maps with different Monte-Carlo runs. The standard deviation of the resulting statistics provided a criterion to define a valid area of the mean vorticity map. Averaging vorticities along circles centered on the south pole returned a zonal vorticity profile for each of the perijoves considered. Averaging the resulting zonal vorticity profiles built the basis for a discussion of the mean profile.</p><p>JunoCam also images the northern hemisphere, at higher resolution but with coverage restricted to a briefer time span and smaller area due to the nature of Juno's elliptical orbit, which will restrict our ability to obtain zonal vorticity profiles.</p>

Self-Guided Stereo Correspondence Estimation Algorithm

This paper introduces an innovative algorithm, “Self-guided Stereo Correspondence” (SGSC), that is directed by photometric properties of the candidate pixels. As the photometric properties of reference image (left image) pixel and its neighbor’s pixel are similar in most cases, so the upcoming corresponding pixel exists in the surrounding of the previous matching pixel. Searching performance is greatly improved by utilizing this photometric property of the candidate pixels. The searching performance is further improved by applying the pioneering threshold technique. These two key techniques sufficiently reduced the computational cost with no degradation of accuracy. The achievements of the proposed method are testified on Middlebury standard Stereo Datasets of 2003 and 2006 and the Middlebury latest Optical Flow Dataset. Finally, the proposed method is compared with present state-of-the-art methods and our SGSC outdoes the latest methods in terms of speed, visualization of hidden ground truth, 3D reconstruction and accuracy.

Stereo Correspondence Estimation by Two Dimensional Real Time Spiral Search Algorithm

This paper presents a new searching algorithm titled “Two Dimensional Real Time Spiral Search Algorithm (2DRTSSA)” to compute the stereo correspondence or dense disparity map of two rectified images. The proposed algorithm can estimate the minimum stereo correspondence or disparity among all the window costs of a fixed axis from minimum to maximum range of that axis. It can also simultaneously calculate the dense disparity of another axis with the same range of axis. So the proposed method calculates stereo correspondence two dimensionally at a time and thus it increases the speed and accuracy over the existing state-of-the-arts methods of one dimensional and left-right searching strategy. The 2DRTSSA method calculates firstly the two window costs; one is along with the +x direction and another is along with –y direction .The minimum disparity of estimated two window costs and their distance parameters are remaining contribute in final selection. The rest of two window costs of –x direction and +y direction are also calculated using the same procedure. The minimum disparity of newly estimated two window costs and their distance are remaining contribute in final selection. The process is then repeated for the successive pixels of reference image along with the 2D scan lines from left to right of the whole image. The 2DRTSSA method is able to optimize the speed and accuracy of estimated dense disparity. Experimental results are compared in Section-IV (A), Section-IV (B) and Section-IV(C) with the current state-of-the-arts methods those are tested on Middlebury Standard stereo data set. The proposed 2DRTSSA method establishes the highest speed and accuracy with properly reconstructed 3D of dense disparity image.

Specialization of mid-tier stages of dorsal and ventral pathways in stereoscopic processing

The division of labor between the dorsal and ventral visual pathways is an influential model of parallel information processing in the cerebral cortex. However, direct comparison of the two pathways at the single-neuron resolution has been scarce. Here we compare how MT and V4, mid-tier areas of the two pathways in the monkey, process binocular disparity, a powerful cue for depth perception and visually guided actions. We report a novel tradeoff where MT neurons transmit disparity signals quickly and robustly, whereas V4 neurons markedly transform the nature of the signals with extra time to solve the stereo correspondence problem. Therefore, signaling speed and robustness are traded for computational complexity. The key factor in this tradeoff was the shape of disparity tuning: V4 neurons had more even-symmetric tuning than MT neurons. Moreover, this correlation between tuning shape and signal transformation was present across individual neurons within both MT and V4. Overall, our results reveal both distinct signaling advantages and common tuning-curve features of the dorsal and ventral pathways in stereoscopic processing.