https://www.jstage.jst.go.jp/article/jsprs1975/37/6/37_6_39/_pdf

1998 IEEE International Conference on SMCに参加して

Deep neural networks have shown excellent performance for stereo matching. Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cost aggregation which is crucial for stereo matching. In this paper, we present a learning-based cost aggregation method for stereo matching by a novel sub-architecture in the end-to-end trainable pipeline. We reformulate the cost aggregation as a learning process of the generation and selection of cost aggregation proposals which indicate the possible cost aggregation results. The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals. The criterion for the selection is determined by the low-level structure information obtained from a light convolutional network. The two-stream network offers a global view guidance for the cost aggregation to rectify the mismatching value stemming from the limited view of the matching cost computation. The comprehensive experiments on challenge datasets such as KITTI and Scene Flow show that our method outperforms the state-of-the-art methods.

Deep Stereo Matching With Explicit Cost Aggregation Sub-Architecture

Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

This paper presents an approach for the estimation of visual motion over an image sequence in real-time. A new algorithm is proposed which solves the correspondence problem between two images in a very efficient way. The method uses the Census Transform as the representation of small image patches. These primitives are matched using a table based indexing scheme. We demonstrate the robustness of this technique on real-world image sequences of a road scenario captured from a vehicle based on-board camera. We focus on the computation of the optical flow. Our method runs in real-time on general purpose platforms and handles large displacements.

Efficient computation of optical flow using the Census Transform

An apparatus for generating depth image is provided, the apparatus according to an exemplary embodiment of the present disclosure being configured to perform an accurate stereo matching even in a low light level by obtaining RGB images and/or IR images, and using the obtained RGB images and/or IR images to extraction of a depth image.

Apparatus for Generating Depth Image

The stereo matching algorithms usually do not satisfy the performance of both accuracy and complexity. The semi-global matching (SGM) is the most efficient method among the stereo matching algorithms. However, it still does not utilize the whole image information to estimate the disparity values. In this paper, we proposed a new concept of the cost aggregation method using summed area table (SAT) scheme. The SAT is the efficient algorithm for summing intensities on the rectangular area in the image. Using four kinds of the SAT arrays (we called these arrays cost aggregation table (CAT)), it is possible to estimate the disparity values with aggregating every cost in the image. We tested our algorithm using the KITTI vision benchmark suite, the result shows that our algorithm is superior for disparity accuracy compared to the SGM. We expect that the CAT can be an alternative cost aggregation method to the SGM in the near future.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-14249-4_78.pdf

Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence

In this paper, a new Advanced Driver Assistant System (ADAS) system for LED traffic sign detection algorithm using rectangle shape based on a windowed hough transform and feature based optimization was presented. We used two character to detect the LED traffic sign. One is LED traffic sign have rectangle shape, another is intensity feature of LED traffic sign. After extracting the candidates of LED traffic signs, our proposed system classify the positive and negative rectangle candidate as a LED traffic sign. Under this flow, we can finally obtained LED traffic sign from real road scene that include LED traffic sign. Our proposed technique was tested in 87 number of real road scene that include LED traffic signs. We can find the 368 number of LED traffic signs of existing 430 number of LED traffic signs. The detection ratio is 85.37%. Algorithm proposed in this paper is very meaningful as a first attempt to detect the LED traffic signs.Detection ratio also reasonable to recognize the traffic sign in the next step of Traffic Sign Recognition (TSR).

LED Traffic Sign Detection Using Rectangular Hough Transform

An apparatus and a method for hierarchical stereo matching are provided. In the method, a reduced image is formed by reducing left and right images, and a first Trellis is performed on the reduced image. Then, a magnified image is generated by magnifying the size and the brightness of the reduced image, and a second Trellis is performed on the magnified image.

Apparatus and method for hierarchical stereo matching

We present a method that can detect a circular traffic signs and Light Emitting Diode (LED) traffic signs by combining fast radial symmetric transform and a proposed symmetric shape detection method The proposed method can choose the appropriate size and sign type by computing the confidence of given image to each pre-defined signs, In a real road video that includes LED signs and circular traffic signs, our algorithm scored 943 % detection ratio and 033 false positive per frame; these results are acceptable in advanced driver assistance systems

LED traffic sign detection with the fast radial symmetric transform and symmetric shape detection

We suggest an efficient real-time method for removing occlusions when synthesizing intermediate views from two stereo images by stereo matching algorithm. The proposed method can carry out the correction of noise resulting from stereo matching, removal of occlusions to achieve high fidelity rendering, and parallel computing to achieve real-time synthesis. The proposed algorithm is compared with state-of-the-art algorithms to show its improved performance in terms of rendering and computational speed.

Hong Jeong

Papers

Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence

LED Traffic Sign Detection Using Rectangular Hough Transform

Apparatus and method for hierarchical stereo matching

LED traffic sign detection with the fast radial symmetric transform and symmetric shape detection

Image synthesis and occlusion removal of intermediate views by stereo matching