Panasonic R&D Center Singapore has harnessed great leaders as much as it has great talent. These key figures lead major research and development projects at the top of their individual fields. Commanding authority and notable achievements in the industry, we are proud that they are a core part of the team.
Local Binary Pattern features for pedestrian detection at night/dark environment
Yunyun Cao, Sugiri Pranata, Hirofumi Nishimura
International Conference on Image Processing (ICIP) 2011, 2011.09
Local Binary Pattern features for pedestrian detection at night/dark environment
Yunyun Cao, Sugiri Pranata, Hirofumi Nishimura
International Conference on Image Processing (ICIP) 2011, 2011.09
Being fast to compute and simple to implement, Local Binary Pattern (LBP) has also shown superior performance in texture classification and face detection. However, it is not well optimized for pedestrian detection. At night/dark environment, pedestrian detection typically needs to overcome problems of low contrast, image blur, and image noise. A novel feature extraction method, consisting of Weighted LBP, Multi-resolution LBP, and Multi-scale LBP, is proposed to solve them. Experimental results show that the proposed method improves upon the basic LBP significantly and outperforms benchmarks such as HOG and CoHOG.
Context constrained facial landmark localization based on discontinuous Haar-like feature
Xiaowei Zhao, Xiujuan Chai, Zhiheng Niu, Cherkeng Heng, Shiguang Shan
Face and Gesture 2011, 2011.03
Facial landmark localization is well known as one of the bottlenecks in face recognition. This paper proposes a novel facial landmark localization method, which introduces facial context constrains into cascaded AdaBoost framework. The motivation of our method lies in the basic human physiology observation that not only the local texture information but also the global context information is used together for human to realize the landmark location task. Therefore, in our solution, a novel type of Haar-like feature, called discontinuous Haar-like feature, is proposed to characterize the facial context, i.e. the cooccurrence relationship between target facial landmark and other local texture patterns within face region (including other landmarks, facial organs and also smoothing regions). For the locating task, traditional Haar-like features (characterizing local texture information) and discontinuous Haar-like features (characterizing context constrains in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, distinctive features are selected automatically and used for facial landmark detection. Our experiments on BioID and Cohn-Kanade databases have validated the proposed method by comparing with other state-of-the-art results.
Fast object detection using boosted co-occurrence histograms of oriented gradients
Haoyu Ren, Cher Keng Heng, Wei Zheng, Luhong Liang, Xilin Chen
International Conference on Image Processing (ICIP) 2010, 2010.12
Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with variable-location and variable-size blocks to capture salient characteristics of the object structure. We consider a CoHOG feature as a block with a special pattern described by the offset. A boosting algorithm is further introduced to select the appropriate locations and offsets to construct an efficient and accurate cascade classifier. Experimental results on public datasets show that our approach simultaneously achieves high accuracy and fast speed on both pedestrian detection and car detection tasks.
A Sample Pre-mapping Method Enhancing Boosting for Object Detection
Haoyu Ren, Xiaopeng Hong, Cher-Keng Heng, Luhong Liang, Xilin Chen
International Conference on Pattern Recognition (ICPR) 2010, 2010.08
We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample’ before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common used methods on public datasets in both pedestrian detection and car detection.
CFA-based Motion Blur Removal using Long/Short Exposure Pairs
Pongsak Lasang, Chinphek Ong, and Shengmei Shen
IEEE Transaction on Consumer Electronics, vol. 56, no. 2, May 2010, pp. 332-338
CFA-based Motion Blur Removal using Long/Short Exposure Pairs
Pongsak Lasang, Chinphek Ong, and Shengmei Shen
IEEE Transaction on Consumer Electronics, vol. 56, no. 2, May 2010, pp. 332-338
This paper presents an efficient and effective motion blur removal method based on long and short exposure images. The two images are captured sequentially and motion pixels between the images are then robustly detected, with suppression of noise and prevention of artifacts around object boundaries. Object motion blur is removed and high quality image is obtained by merging the two images with taking into account the detected motion pixels. The proposed method is directly performed on the CFA (Color Filter Array) image which only has one color component per pixel. It has low computational complexity and low memory requirements. The proposed method also achieves a HDR (High Dynamic Range) image at the same time.
Pongsak Lasang, Chinphek Ong, and Shengmei Shen
Proc. IEEE International Conference on Consumer Electronics (ICCE’10), January 2010, pp.37-38.
In this paper, a simple and effective motion blur removal method based on long and short exposure images is presented. The long and short exposure images are captured sequentially. Motion pixels between the images are robustly detected, with suppressing noise and preventing artifacts around object boundary. The object motion blur is removed and high quality image is obtained by merging the two images with takes into account the detected motion pixels. The proposed method is directly performed on the CFA (Color Filter Array) image which is only one color component per pixel. It has low computational complexity and memory requirements. The proposed method can achieve HDR (High Dynamic Range) image at the same time.
Rate-Complexity Scalable Multi-view Image Coding with Adaptive Disparity-Compensated Wavelet Lifting
Pongsak Lasang, Chang-su Kim, and Wuttipong Kumwilaisak
Journal of Information and Computing Science, vol. 4, no. 3, August 2009, pp. 211-223
In this paper, a simple and effective motion blur removal method based on long and short exposure images is presented. The long and short exposure images are captured sequentially. Motion pixels between the images are robustly detected, with suppressing noise and preventing artifacts around object boundary. The object motion blur is removed and high quality image is obtained by merging the two images with takes into account the detected motion pixels. The proposed method is directly performed on the CFA (Color Filter Array) image which is only one color component per pixel. It has low computational complexity and memory requirements. The proposed method can achieve HDR (High Dynamic Range) image at the same time.
Rate Distortion Analysis and Bit Allocation Scheme for Wavelet Lifting-Based Multiview Image Coding
Pongsak Lasang and Wuttipong Kumwilaisak
EURASIP Journal on Advances in Signal Processing, Volume 2009 (2009), pp. 1-13, 2009.01
This paper studies the distortion and the model-based bit allocation scheme of wavelet lifting-based multiview image coding. Redundancies among image views are removed by disparity-compensated wavelet lifting (DCWL). The distortion prediction of the low-pass and high-pass subbands of each image view from the DCWL process is analyzed. The derived distortion is used with different rate distortion models in the bit allocation of multiview images. Rate distortion models including power model, exponential model, and the proposed combining the power and exponential models are studied. The proposed rate distortion model exploits the accuracy of both power and exponential models in a wide range of target bit rates. Then, low-pass and high-pass subbands are compressed by SPIHT (Set Partitioning in Hierarchical Trees) with a bit allocation solution. We verify the derived distortion and the bit allocation with several sets of multiview images. The results show that the bit allocation solution based on the derived distortion and our bit allocation scheme provide closer results to those of the exhaustive search method in both allocated bits and peak-signal-to-noise ratio (PSNR). It also outperforms the uniform bit allocation and uniform bit allocation with normalized energy in the order of 1.7–2 and 0.3–1.4 dB, respectively.
