Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.

title: Hierarchical model based human motion tracking
abstract: Image sequence based tracking is the pivotal technique of human motion. In the paper, we first propose an appropriate human model and color model. Second, two approaches are proposed aiming at two different levels of color-block information including the boundary and the inner-area: the extraction of the boundary algorithm is based on the Robert operator and the clustering algorithm is based on self-adaptation. Third, we unite two regions, which are processed by different approaches. This step counteracts the ambiguity of obtaining information from each level. Finally, after the rough area of the color-block is obtained, the boundary of the color-block is determined by calculating the histogram of the X,Y coordinate of every point on the boundary of the block. Experimental results are presented.

title: A unified adaptive approach to accurate skin detection
abstract: Due to variations of lighting conditions and camera hardware settings and the existence of many ethnic people with a wide range of skin colors, a generic skin model is often inadequate to accurately capture the skin distribution for individual images. In this paper, we propose an adaptive skin detection framework, which allows modeling title skin distribution with significantly higher accuracy and flexibility. First, an adaptive skin model, specific to the image under consideration and refined from the skin-similar space, is derived using a Gaussian mixture model (GMM) and standard expectation maximization (EM) algorithm. Then, we develop a support vector machine (SVM) classifier to identify the skin Gaussian from the trained GMM (with two Gaussian components) by incorporating spatial and shape information of skin pixels. Extensive experimental results performed on large image databases have demonstrated the effectiveness and benefits of the proposed approach.

title: A new approach for rotated face detection
abstract: Human face detection has always been an important problem for face recognition and face tracking. Though considerable attempts have been made to detect and localize faces, these approaches have made assumptions that restrict their extension to more general cases. In this paper we design a novel method to detect a rotated face on the basis of image edge information. Considering the efficiency problem, we propose two key techniques in our approach: first, three points based RHT is applied to detect an ellipse; moreover, we speedup the template matching procedure through calculating the orientation histogram distribution and locating the symmetry axis of a rotated face. In the end, we will validate our idea by several experiment results.

title: Exploiting Similarity of Subqueries for Complex Query Optimization
abstract: Query optimizers in current database management systems (DBMS) often face problems such as intolerably long optimization time and/or poor optimization results when optimizing complex queries. To tackle this challenge, we present a new similarity-based optimization technique in this paper. The key idea is to identify groups of similar subqueries that often appear in a complex query and share the optimization result within each group in the query. An efficient algorithm to identify similar queries in a given query and optimize the query based on similarity is presented. Our experimental results demonstrate that the proposed technique is quite promising in optimizing complex queries in a DBMS.

title: Long-Term Temporal Features for Conversational Speech Recognition
abstract: The automatic transcription of conversational speech, both from telephone and in-person interactions, is still an extremely challenging task. Our efforts to recognize speech from meetings is likely to benefit from any advances we achieve with conversational telephone speech, a topic of considerable focus for our research. Towards both of these ends, we have developed, in collaboration with our colleagues at SRI and IDIAP, techniques to incorporate long-term (~500 ms) temporal information using multi-layered perceptrons (MLPs). Much of this work is based on prior achievements in recent years at the former lab of Hynek Hermansky at the Oregon Graduate Institute (OGI), where the TempoRAl Pattern (TRAP) approach was developed. The contribution here is to present experiments showing: 1) that simply widening acoustic context by using more frames of full band speech energies as input to the MLP is suboptimal compared to a more constrained two-stage approach that first focuses on long-term temporal patterns in each critical band separately and then combines them, 2) that the best two-stage approach studied utilizes hidden activation values of MLPs trained on the log critical band energies (LCBEs) of 51 consecutive frames, and 3) that combining the best two-stage approach with conventional short-term features significantly reduces word error rates on the 2001 NIST Hub-5 conversational telephone speech (CTS) evaluation set with models trained using the Switchboard Corpus.

title: Developing cost models with qualitative variables for dynamic multidatabase environments
abstract: A major challenge for global query optimization in a multidatabase system (MDBS) is the lack of local cost information at the global level due to local autonomy. A number of methods to derive local cost models have been suggested recently. However, these methods are only suitable for a static multidatabase environment. In this paper, we propose a new multi-state query sampling method to develop local cost models for a dynamic environment. The system contention level at a dynamic local site is divided into a number of discrete contention states based on the cost of a probing query. To determine an appropriate set of contention states for a dynamic environment, two algorithms based on iterative uniform partitioning and data clustering, respectively, are introduced. A qualitative variable is used to indicate the contention states for the dynamic environment. The techniques from our previous (static) query sampling method, including query sampling, automatic variable selection, regression analysis and model validation, are extended so as to develop a cost model incorporating the qualitative variable for a dynamic environment. Experimental results demonstrate that this new multi-state query sampling method is quite promising in developing useful cost models for a dynamic multidatabase environment.

title: A Fuzzy Query Optimization Approach for Multidatabase Systems
abstract: A crucial challenge for global query optimization in a multidatabase system (MDBS) is that some local optimization information, such as local cost parameters, may not be accurately known at the global level because of local autonomy. Traditional query optimization techniques using a crisp cost model may not be suitable for an MDBS because precise information is required. In this paper we present a new approach that performs global query optimization using a fuzzy cost model that allows fuzzy information. We suggest methods for establishing a fuzzy cost model and introduce a fuzzy optimization criterion that can be used with a fuzzy cost model. We discuss the relationship between the fuzzy optimization approach and the traditional (crisp) optimization approach and show that the former has a better chance to find a good execution strategy for a query in an MDBS environment, but its complexity may grow exponentially compared with the complexity of the later. To reduce the complexity, we suggest to use so-called k-approximate fuzzy values to approximate all fuzzy values during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.

title: Piggyback Statistics Collection for Query Optimization: Towards a Self-Maintaining Database Management System
abstract: A database management system (DBMS) performs query optimization based on statistical information about data in the underlying database. Out-of-date statistics may lead to inefficient query processing in the system. The existing utility method, which collects statistics in batch mode, suffers from drawbacks such as heavy administrative burden, high system load and tardy updates. In this paper, we study approaches to performing statistical analysis on the fly during query execution, taking advantage of data already resident in main memory. We propose a framework for on-the-fly statistics collection, which we term piggybacking, and analyze the tradeoffs of piggybacking various statistics collection techniques on top of query execution plans. We present a multiple-granularity interleaving algorithm to integrate a set of piggyback operations with an execution plan, and show how the algorithm can be incorporated into an existing query optimizer. Our experiments demonstrate that useful statistics can be obtained via the piggyback method with a small overhead.