객체 인식과 추적(object recognition and tracking)은 컴퓨터 비전의 중요 분야로써 작게는 동작 인식으로부터 크게는 우주 항공까지 그 활용 가능성이 무궁무진하다. 객체 인식의 정확도를 향상시키는 방법 중 하나는 방향, 스케일 그리고 가려짐에 강건한 컬러를 이용하는 것이다. 컬러를 이용함으로써 더 많은 특징점들을 추출하기 위한 계산 비용을 감소시킬 수 있다. 또한, 빠른 객체 인식을 위해 알고리즘의 정확도를 낮추는 것보다 객체의 위치를 예측하고 좀 더 작은 영역에서 인식을 수행하는 것이 더욱 효과적이다. 본 논문은, 인식 정확도를 향상시키기 위해 대표적인 객체 인식 알고리즘인 SURF와 컬러모델을 적용한 기술자(descriptor)를 사용하고, 움직임 예측 알고리즘인 Kalman filter를 결합하여 빠른 객체 추적 방법을 제안한다. 그 결과, 제안하는 방법은 다른 컬러를 갖는 같은 패턴의 객체들을 구분하고, 객체의 향후 움직임을 미리 예측한 관심영역(ROI)에서 인식을 수행함으로써 빠른 추적 결과를 보였다.

SURF(Speeded Up Robust Features)와 Kalman Filter를 이용한 컬러 객체 추적 방법의 제안

The CAPTCHA has become an important issue in multimedia security. Aimed at a commonly used text-based CAPTCHA, this paper outlines some typical methods and summarizes the technological progress in text-based CAPTCHA breaking. First, the paper presents a comprehensive review of recent developments in the text-based CAPTCHA breaking field. Second, a framework of text-based CAPTCHA breaking technique is proposed. And the framework mainly consists of preprocessing, segmentation, combination, recognition, postprocessing, and other modules. Third, the research progress of the technique involved in each module is introduced, and some typical methods of segmentation and recognition are compared and analyzed. Lastly, the paper discusses some problems worth further research.

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A Survey on Breaking Technique of Text-Based CAPTCHA

The process to locate objects in an image passes through different phases. At the forefront of these phases, and most importantly, is the edge detection. If edges in an image are identified accurat...

Image Edge Detectors under Different Noise Levels with FPGA Implementations

A survey of CAPTCHA technologies to distinguish between human and computer

Biometric deals with the verification and identification of a person based on behavioural and physiological traits. This article presents recent advances in physiological-based biometric multimodalities, where we focused on finger vein, palm vein, fingerprint, face, lips, iris, and retina-based processing methods. The authors also evaluated the architecture, operational mode, and performance metrics of biometric technology. In this article, the authors summarize and study various traditional and deep learning-based physiological-based biometric modalities. An extensive review of biometric steps of multiple modalities by using different levels such as preprocessing, feature extraction, and classification, are presented in detail. Challenges and future trends of existing conventional and deep learning approaches are explained in detail to help the researcher. Moreover, traditional and deep learning methods of various physiological-based biometric systems are roughly analyzed to evaluate them. The comparison result and discussion section of this article indicate that there is still a need to develop a robust physiological-based method to advance and improve the performance of the biometric system.

A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends

CAPTCHA is a security technique to allow a computer application to distinguish between computer and human access. Most of now a day CAPTCHA words have their characters connected together which makes them very difficult to be segmented and recognized. In this paper we present an efficient technique that first benefits from the intersections between characters in a word and then segment it based on the recognition of each segmented character. Edge corners ECs are used in both segmentation and recognition phases. A novel fuzzy logic-based scheme is proposed to match characters using their ECs. Experimental results show the efficiency of the proposed scheme in terms of success rate using a large set of CAPTCHA's words. Copyright © 2015 John Wiley & Sons, Ltd.

Breaking down Captcha using edge corners and fuzzy logic segmentation/recognition technique

This paper presents a new algorithm for image co-registration using dominant corners located on the image's edges under the assumption that the deformation between the successive images is modeled by an affine transformation. This assumption is guaranteed when the time interval between acquired images is small like in a video sequence. Therefore, it is mainly dedicated for motion analysis. The method detects first straight edges of an object contour then classifies their intersection points as contour's corners characterized by their angles and adjacent segments lengths. The suppressor starts then to eliminate iteratively the weak corners until reaching a set of dominant ones called "Dominant Corners". These dominant corners are shown to be very repeatable under affinity transformation. A Primitive is constructed by four consecutive dominant corners located on the same contour. The invariant measure that characterizes each primitive is the ratio of areas of two triangles constructed by two triplets selected from these four corners and the corners directions difference. The primitives are formed in the two studied images from an image sequence. All primitives are used to vote for the best affine model that relates the two images. The method used in primitive construction will lead to an important enhancement over existing methods in the voting process in time and accuracy. This method is tested on real images and good results are reported.

Towards an automatic image co-registration technique using edge dominant corners primitives

This paper presents a novel method to find corners that are well located and stable interest points in a given image. Our corners are defined as intersection points of non collinear straight image edges, which are very robust against various image transformations like image scaling, rotation, translation and also to viewpoint and illumination changes. Some light updates on the linking edge step that should be applied in order to extract edges and their intersections that construct the searched corners are also discussed. Experiments conducted on real images demonstrate that the proposed method is able to achieve a very good performance in accuracy, stability and especially computational efficiency in comparison with existing methods.

A robust edge based corner detector

A new algorithm to detect straight edge parts which form the contour of an object presented in an image is discussed in this paper. This algorithm is very robust and can detect true straight edges even when their pixel's locations are not straight due to natural noise at the object borders. These straight edges are than used to report and classify contour's corners according to their angle and their adjacent segments lengths. A new technique for polygonal approximation is also presented to find the best set among these corners to construct the polygon vertices that best describe the approximating contour. It starts by eliminating the corners, one after the other using Iterative Corner Suppression (ICS) process. This in turn enables us to obtain the smallest possible error in the approximation. Experimental results demonstrate the efficiency of this technique in comparison with recently proposed algorithms.

Polygonal approximation of an object contour by detecting edge dominant corners using Iterative Corner Suppression

This paper presents a new algorithm for image registration working on an image sequence using dominant corners located on the image's edges under the assumption that the deformation between the successive images can be modeled by an affine transformation. To guarantee this assumption, the time interval between acquired images should be small like the time interval in a video sequence. In the edge image, dominant corners are extracted per linked contour and form a polygon that best approximates the current linked contour. The number of these dominant corners per contour is derived automatically given an approximation error. These dominant corners are shown to be very repeatable under affinity transformation. Then, a Primitive is constructed by four dominant corners. The invariant measure that characterizes each primitive is the ratio of areas of two triangles constructed by two triplets selected from these four corners.

Rabih Al Nachar

Papers

Breaking down Captcha using edge corners and fuzzy logic segmentation/recognition technique

Towards an automatic image co-registration technique using edge dominant corners primitives

A robust edge based corner detector

Polygonal approximation of an object contour by detecting edge dominant corners using Iterative Corner Suppression

Image registration based on edge dominant corners