林智仁LIBSVM的相关论文及说明文档

收集了一些关于林智仁关于他的LIBSVM的论文

行人检测综述论文（中英）

收集了一些关于行人检测的中英文的综述论文

关于行人检测方面国际会议论文

下载了一些近几年的国际会议的关于行人检测的英文论文

基于全局对比度的显著性区域检测与分割的相关论文和程序（C++）

这是程明明的2011年CVPR的论文及其相关的一些论文，还有程的C++代码。

Face Detection with a 3D Model

Abstract This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on Local Binary features relative to the 3D pose. A compatible subset of candidates is then obtained by non-maximal suppression. Experiments on two standard face detection datasets show that the proposed 3D model based approach obtains results comparable to state of the art.

A Convolutional Neural Network Cascade for Face Detection

In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, de- mand an advanced discriminative model to accurately dif- ferentiate faces from the backgrounds. Consequently, ef- fective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discrimina- tive capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low res- olution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the sub- sequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection per- formance on two public face detection benchmarks.

Aggregate Channel Features for Multi-view Face Detection

Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more pow- erful learning algorithms, the feature representation used for face detection still can’t meet the demand for effectively and efficiently handling faces with large appearance vari- ance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradi- ent magnitude and oriented gradient histograms and there- fore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multi- scale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detec- tion adjustment. Following the learning pipelines in Viola- Jones framework, the multi-view face detector using ag- gregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB test- sets, while runs at 42 FPS on VGA images.

A Survey of Recent Advances in Face Detection

Face detection has been one of the most studied topics in the computer vision literature. In this technical report, we survey the recent advances in face detection for the past decade. The seminal Viola-Jones face detector is first re- viewed. We then survey the various techniques according to how they extract features and what learning algorithms are adopted. It is our hope that by reviewing the many existing algorithms, we will see even better algorithms developed to solve this fundamental computer vision problem. 1

face_detection_with_supplementary_material

Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two surprising new top performance results. First, we show that a properly trained vanilla DPM reaches top performance, improving over commercial and research systems. Second, we show that a detector based on rigid templates - similar in structure to the Viola

RFID的智能图书馆系统

感知校园中基于RFID的智能图书馆系统客户端的研究与实现