Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Digital Coding of Waveforms

Traditional and recent approaches in background modeling for foreground detection: An overview

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

A vehicular vision system includes a camera having a lens and a CMOS photosensor array having a plurality of photosensor elements. The camera is disposed at an interior portion of a vehicle equipped with the vehicular vision system. The camera one of (i) views exterior of the equipped vehicle through the windshield of the equipped vehicle and forward of the equipped vehicle and (ii) views from the windshield of the equipped vehicle into the interior cabin of the equipped vehicle. A control includes an image processor that processes image data captured by the photosensor array. The image processor processes captured image data to detect an object viewed by the camera. The photosensor array is operable at a plurality of exposure periods and at least one exposure period of the plurality of exposure periods is dynamically variable.

Vehicular vision system

An efficient moving object segmentation algorithm suitable for real-time content-based multimedia communication systems is proposed in this paper. First, a background registration technique is used to construct a reliable background image from the accumulated frame difference information. The moving object region is then separated from the background region by comparing the current frame with the constructed background image. Finally, a post-processing step is applied on the obtained object mask to remove noise regions and to smooth the object boundary. In situations where object shadows appear in the background region, a pre-processing gradient filter is applied on the input image to reduce the shadow effect. In order to meet the real-time requirement, no computationally intensive operation is included in this method. Moreover, the implementation is optimized using parallel processing and a processing speed of 25 QCIF fps can be achieved on a personal computer with a 450-MHz Pentium III processor. Good segmentation performance is demonstrated by the simulation results.

https://video.ee.ntu.edu.tw/publication/paper/[J][2002][TCSVT][Shao-Yi.Chien][1].pdf

Efficient moving object segmentation algorithm using background registration technique

In the new video coding standard H.264/AVC, motion estimation (ME) is allowed to search multiple reference frames. Therefore, the required computation is highly increased, and it is in proportion to the number of searched reference frames. However, the reduction in prediction residues is mostly dependent on the nature of sequences, not on the number of searched frames. Sometimes the prediction residues can be greatly reduced, but frequently a lot of computation is wasted without achieving any better coding performance. In this paper, we propose a context-based adaptive method to speed up the multiple reference frames ME. Statistical analysis is first applied to the available information for each macroblock (MB) after intra-prediction and inter-prediction from the previous frame. Context-based adaptive criteria are then derived to determine whether it is necessary to search more reference frames. The reference frame selection criteria are related to selected MB modes, inter-prediction residues, intra-prediction residues, motion vectors of subpartitioned blocks, and quantization parameters. Many available standard video sequences are tested as examples. The simulation results show that the proposed algorithm can maintain competitively the same video quality as exhaustive search of multiple reference frames. Meanwhile, 76 %-96 % of computation for searching unnecessary reference frames can be avoided. Moreover, our fast reference frame selection is orthogonal to conventional fast block matching algorithms, and they can be easily combined to achieve further efficient implementations.

/pdf/analysis-and-complexity-reduction-of-multiple-reference-1xhvn6pr3u.pdf

Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC

An H.264/AVC encoder is implemented on a 31.72mm/sup 2/ die with 0.18/spl mu/m CMOS technology. A four-stage macroblock pipelined architecture encodes 720p 30f/s HDTV videos in real time at 108MHz. The encoded video quality is competitive with reference software requiring 3.6TOPS on a general-purpose processor-based platform.

A 1.3TOPS H.264/AVC single-chip encoder for HDTV applications

Automatic video segmentation plays an important role in real-time MPEG-4 encoding systems. Several video segmentation algorithms have been proposed; however, most of them are not suitable for real-time applications because of high computation load and many parameters needed to be set in advance. This paper presents a fast video segmentation algorithm for MPEG-4 camera systems. With change detection and background registration techniques, this algorithm can give satisfying segmentation results with low computation load. The processing speed of 40 QCIF frames per second can be achieved on a personal computer with an 800 MHz Pentium-III processor. Besides, it has shadow cancellation mode, which can deal with light changing effect and shadow effect. A fast global motion compensation algorithm is also included in this algorithm to make it applicable in slight moving camera situations. Furthermore, the required parameters can be decided automatically, which can enhance the proposed algorithm to have adaptive threshold ability. It can be integrated into MPEG-4 videophone systems and digital cameras.

/pdf/fast-video-segmentation-algorithm-with-shadow-cancellation-eo4gn16ud6.pdf

Fast video segmentation algorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques

In the new video coding standard, MPEG-4 AVC/JVT/H264, motion estimation is allowed to use multiple reference frames The reference software adopts full search scheme, and the increased computation is in proportion to the number of searched reference frames However, the reduction of prediction residues is highly dependent on the nature of sequences, not on the number of searched frames In this paper, we present a method to speed up the matching process for multiple reference frames For each macroblock, we analyze the available information after intra prediction and motion estimation from previous one frame to determine whether it is necessary to search more frames The information we use includes selected mode, inter prediction residues, intra prediction residues, and motion vectors Simulation results show that the proposed algorithm can save up to 90% of unnecessary frames while keeping the average miss rate of optimal frames less than 4%

Shyh-Yih Ma

Papers

Efficient moving object segmentation algorithm using background registration technique

Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC

A 1.3TOPS H.264/AVC single-chip encoder for HDTV applications

Fast video segmentation algorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques

Analysis and reduction of reference frames for motion estimation in MPEG-4 AVC/JVT/H.264