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Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
This paper proposes to enhance the existing methods of Self-Supervised Learning (SSL) with application to autonomous navigation systems through efficient computational approaches that are the principal requirements in a practical system. First, confidence-based auto labeling for self-supervised learning is introduced which identifies and eliminates the input samples with low confidence level that...
This paper propose a method for automatic vehicle detection in QuickBird images of small highways, with relatively low traffic density, frequent occurrence of tree shadows, and changes in illumination conditions. The vehicle detection is based on an elliptical Laplacian of Gaussian scale space methodology, where the vehicle locations are detected at local extrema in the image response to convolution...
In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called "decompose learning" is proposed...
The capability to visually discern possible obstacles from the sky would be a valuable asset to a UAV for avoiding both other flying vehicles and static obstacles in its environment. The main contribution of this article is the presentation of a feasible approach to obstacle avoidance based on the segmentation of camera images into sky and non-sky regions. The approach is named the Sky Segmentation...
We develop a new perspective invariant feature space representation of remotely sensed objects, regarding the features themselves as primitive observables of the 3D objects and to estimate them from multiple sensor measurements. This is formulated as an inverse problem in the feature coefficients. Once the coefficients are estimated they may be used to derive higher level features used by machine...
Facial rejuvenation has driven a lot of research in the field of dermatology and plastic surgery, leading to many medical procedures. This paper proposes an age prediction method that could be used to better understand the ageing process and to evaluate the benefits of a rejuvenating treatment, for example. A supervised Facial Model (SFM) is built using Partial Least Squares regression (PLSR) to capture...
In computer vision, many applications could greatly benefit from multi-spectral image data. Our aim is to illustrate the effectiveness of multi-spectral analysis obtained from a simple and cost-effective system. While the proposed approach is broadly applicable, in this paper we focus on the specific case of skin detection. To obtain the multi-spectral data, we have assembled a system using multiple...
Smile detection in real-life face images is an interesting problem with many potential applications. This paper presents an efficient approach to smile detection for face images captured in real-world unconstrained scenarios. In our approach, the pixel intensities in the gray-scale face image are compared, and the intensity differences are used as features. We adopt Adaboost to choose and combine...
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...
This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive...
This paper describes a large-scale experimental study, in which a humanoid robot learned to press and detect doorbell buttons autonomously. The models for action selection and visual detection were grounded in the robot's sensorimotor experience and learned without human intervention. Experiments were performed with seven doorbell buttons, which provided auditory feedback when pressed. The robot learned...
In this paper, SIFT algorithm is combined with AdaBoost algorithm, and a method of feature matching based on multi-pose face is put forward. Firstly, the face region is extracted from multi-pose face images by AdaBoost. Secondly, SIFT characteristic vectors of the main regions are matched. The images of the ORL face DB are used in this paper, and some pictures taken in the experiment are used too...
The design of traffic sign recognition (TSR) system, one important subsystem of Advanced Driver Assistance System (ADAS), has been a challenge practical problem for many years due to the complex issues like road environments, lighting conditions, occlusion, and so on. In this paper, we introduce a new TSR system, whose effectiveness has been tested through extensive experiments. The established TSR...
Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results...
This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored...
Shadow detection is a critical issue for most applications of video surveillance. In this study, we present an object-wise online learning method to detect casting shadows without providing any priori scene information or threshold parameters. Hue, saturation, and intensity- difference histograms of moving objects are collected to learn a cumulative distribution separately. The accumulating strategy...
In this work we present a new approach for learning a layered stacked graphical model for the problem of visual object detection and segmentation. It is obvious that visual objects can be represented by multiple feature cues, such as color, texture, shape. The idea is to treat different feature types in different processes for learning classifiers and then integrate them into a unified model. We employ...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises...
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