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The pollen grains of different plant taxa exhibit various shapes and sizes. This structural diversity has made the identification and classification of pollen grains an important tool in many fields. Despite the myriad of applications, the classification of pollen grains is still a tedious and time-consuming process that must be performed by highly skilled specialists. In this paper, we propose an...
Pedestrian detection is of much importance for its practical applications. This paper develops a novel pedestrian detection system which consists of three stages: motion region detection based on background modeling, feature extraction in the guidance of prior information, and map-based classification applying support vector machine (SVM) and Adaboost. First of all, an adaptive Gaussian Mixture Model...
State-of-the-art methods for human detection and pose estimation require many training samples for best performance. While large, manually collected datasets exist, the captured variations w.r.t. appearance, shape and pose are often uncontrolled thus limiting the overall performance. In order to overcome this limitation we propose a new technique to extend an existing training set that allows to explicitly...
Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In this paper we present that, besides the local features, context cues in the neighborhood provide important constraints that are not yet well utilized. We propose a framework to incorporate the context constraints for detection...
We present a machine learning framework that automatically generates a model set of landmarks for some class of registered 3D objects: here we use human faces. The aim is to replace heuristically-designed landmark models by something that is learned from training data. The value of this automatically generated model is an expected improvement in robustness and precision of learning-based 3D landmarking...
Extracting and labeling sulcal curves on the human cerebral cortex is important for many neuroscience studies, however manually annotating the sulcal curves is a time-consuming task. In this paper, we present an automatic sulcal curve extraction method by registering a set of dense landmark points representing the sulcal curves to the subject cortical surface. A Markov random field is used to model...
Human age estimation based on face images can figure in a wide variety of real-world applications. In this paper, we propose a novel and efficient facial age estimation algorithm which decides human age in a hierarchical framework. Biologically, human lives can be roughly divided into two stages, the period from birth to adulthood and the period from adulthood to old age, which are quite different...
Swift lets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. To date, the bird nest grading is based on weight, shape and size. The inspection and grading for raw edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments. A Fourier-based shape separation (FD) method was developed from Charge...
This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent...
This paper proposes a novel graph-based method for representing a human's shape during the performance of an action. Despite their strong representational power, graphs are computationally cumbersome for pattern analysis. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by way of...
In this paper we develop an algorithm for action recognition and localization in videos. The algorithm uses a figure-centric visual word representation. Different from previous approaches it does not require reliable human detection and tracking as input. Instead, the person location is treated as a latent variable that is inferred simultaneously with action recognition. A spatial model for an action...
This paper proposes a simple yet novel method for recognition of certain sorts of moving entities incorporating their shape and motion patterns. Although shape features have been commonly employed in object recognition, motion characteristics are in general not integrated to geometric models. In the interest of utilizing the motion attributes, the trajectories are investigated to extract the ‘coherence...
The main contribution of this paper is a new people detection algorithm based on motion information. The algorithm builds a people motion model based on the Implicit Shape Model (ISM) Framework and the MoSIFT descriptor. We also propose a detection system that integrates appearance, motion and tracking information. Experimental results over sequences extracted from the TRECVID dataset show that our...
The bag of words model has been actively adopted by content based image retrieval and image annotation techniques. We employ this model for the particular task of pedestrian detection in two dimensional images, producing this way a novel approach to pedestrian detection. The experiments we have done in this paper compare the behavior of discriminative recognition approaches that use AdaBoost on codebook...
Shape and motion are two most distinct cues observed from human actions. Traditionally, K-Nearest Neighbor (K-NN) classifier is used to compute crisp votes from multiple cues separately. The votes are then combined using linear weighting scheme. Usually, the weights are determined in a brute-force or trial-and-error manner. In this study, we propose a new classification framework based on sum-rule...
Firstly, this paper expounds the basic physical theory from the players, the sports technology aspects, and the quality of physical addresses is in the kinetic theory of the status. The role on the discussion of the physical and technical theory as well as the practice for the sport is in existence. Thus, with the practice of modern sports training, it gives a set of basic theory.
People detection and tracking is a key component for robots and autonomous vehicles in human environments. While prior work mainly employed image or 2D range data for this task, in this paper, we address the problem using 3D range data. In our approach, a top-down classifier selects hypotheses from a bottom-up detector, both based on sets of boosted features. The bottom-up detector learns a layered...
Human movement analysis is a long-studied, but still important and challenging research area in visual surveillance. It involves many fundamental problems in computer vision such as human detection, segmentation and tracking, and higher level problems such as human gesture, action and event recognition. Shape is the most dominant cue for detecting humans due to large appearance variability. In this...
Common human actions are instantly recognizable by people and increasingly machines need to understand this language if they are to engage smoothly with people. Here we introduce a new method for automated human action recognition. The proposed method represents videos as a tangent bundle on a Grassmann manifold. Videos are expressed as third order tensors and factorized to a set of tangent spaces...
Human figure identification is always a challenging move in field of pattern recognition. This paper presents a complete algorithm to find a single object (human body) and identify the object as human being. The algorithm starts the segmentation process with basic frame difference method and use morphological operators, edge detection, feature point generation and finally spline interpolation to find...
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