From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA–SIFT) and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods‟ application in recognition. KNN is used to find the matches, and RANSAC to reject inconsistent matches from which the inliers can take as correct matches. The performance of the robust feature detection methods are compared for scale changes, rotation, and blur. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although it‟s slow. SURF is the fastest one with good performance as the same as SIFT. PCA-SIFT show its advantages in rotation and illumination changes.

A Comparison of SIFT, PCA-SIFT and SURF

Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, the similarities are calculated as the cosine distance between label features of images which are generated by a deep convolutional network (ConvNet). By introducing a constraint into DAC, the learned label features tend to be one-hot vectors that can be utilized for clustering images. The main challenge is that the ground-truth similarities are unknown in image clustering. We handle this issue by presenting an alternating iterative Adaptive Learning algorithm where each iteration alternately selects labeled samples and trains the ConvNet. Conclusively, images are automatically clustered based on the label features. Experimental results show that DAC achieves state-of-the-art performance on five popular datasets, e.g., yielding 97.75% clustering accuracy on MNIST, 52.18% on CIFAR-10 and 46.99% on STL-10.

Deep Adaptive Image Clustering

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching developed by David Lowe (1999,2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey-level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interest point, with the intention that this descriptor should be used for matching corresponding interest points between different images. Later, the SIFT descriptor has also been applied at dense grids (dense SIFT) which have been shown to lead to better performance for tasks such as object categorization and texture classification. The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video.

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Scale Invariant Feature Transform

A new local search based hybrid genetic algorithm for feature selection

Clustering is a task that aims to grouping data objects into several groups. DBSCAN is a density-based clustering method. However, it requires two parameters and these two parameters are hard to decide. Also, DBSCAN has difficulties in finding clusters when the density changes in the dataset. In this paper, we modify the original DBSCAN to make it able to determine the appropriate eps values according to data distribution and to cluster when the density varies among dataset. The main idea is to run DBSCAN with different eps and Minpts values. We also modified the calculation of the Minpts so that DBSCAN can have better clustering results. We did several experiments to evaluate the performance. The results suggest that our proposed DBSCAN can automatically decide the appropriate eps and Minpts values and can detect clusters with different density-levels.

Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to data

Feature selection plays an important role in image retrieval systems. The better selection of features usually results in higher retrieval accuracy. This work tries to select the best feature set from a total of 78 low level image features, including regional, color, and textual features, using the genetic algorithms (GA). However, the GA is known to be slow to converge. In this work we propose two directions to improve the convergence time of the GA. First we employ the Taguchi method to reduce the number of necessary offspring to be tested in every generation in the GA. Second we propose to use an alternative measure, the Hubert's @C statistics, to evaluate the fitness of each offspring instead of evaluating the retrieval accuracy directly. The experiment results show that the proposed techniques improve the feature selection results by using the GA in both time and accuracy.

Feature selection using genetic algorithm and cluster validation

Recently, Activity Recognition (AR) has become a popular research topic and gained attention in the study field because of the increasing availability of sensors in consumer products, such as GPS sensors, vision sensors, audio sensors, light sensors, temperature sensors, direction sensors, and acceleration sensors. The availability of a variety of sensors creates many new opportunities for data mining applications. This paper proposes a mobile phone-based system that employs the accelerometer and the gyroscope signals for AR. To evaluate the proposed system, we employ a data set where 30 volunteers performed daily activities such as walking, lying, upstairs, sitting, and standing. The result shows that the features extracted from the gyroscope enhance the classification accuracy in term of dynamic activities recognition such as walking and upstairs. A comparison study shows that the recognition accuracies of the proposed framework using various classification algorithms are higher than previous works.

Activity Recognition with sensors on mobile devices

Most hand detection and tracking algorithms can be only applied in the fairly simple and similar background. We propose to combine a modified object detection method proposed by Viola and Jones with the skin-color detection method to perform hand detection and tracking against complex background. Out experimental results show that the proposed method is effective in near real-time speed (15 frames per second.).

Real-Time Hand Detection and Tracking against Complex Background

There remain many difficult problems in computer vision research such as object recognition, three dimensional reconstruction, object tracking, etc. And the basis of solving these problems relies on image matching. The scale invariant feature transform (SIFT) algorithm has been widely used for image matching application. The SIFT algorithm can successfully extract the most descriptive feature points in given input images taken under different viewpoints. However, the performance of the original SIFT algorithm degrades under the influence of noise. We propose to modify the SIFT algorithm to produce better invariant feature points for image matching under noise. We also propose to employ the Earth mover's distance (EMD) as the measurement of similarity between two descriptors. We present extensive experiment results to demonstrate the performance of the proposed methods in image matching under noise.

Maw-Kae Hor

Papers

Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to data

Feature selection using genetic algorithm and cluster validation

Activity Recognition with sensors on mobile devices

Real-Time Hand Detection and Tracking against Complex Background

Modified sift descriptor for image matching under interference