Generalizing the Hough transform to detect arbitrary shapes

Ballard, Dana H.

doi:10.1016/0031-3203(81)90009-1

Cited by 3,836 publications

(1,684 citation statements)

References 14 publications

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“…To find eyeballs in obtain eye area in an image proposed system uses circular Hough Transform. The Hough transform was first introduced by Paul Hough in 1962 [14]. HTC The equation of a circle can be written as r² = (x -a) ² + (y -b) ² In above equation a and b are the coordinates for the center, while r is radius of the circle.…”

Section: Figure5 Cascade Of Classifier [E] Eye Detectionmentioning

confidence: 99%

A Real Time Driver's Eye Tracking Design Proposal for Detection of Fatigue Drowsiness

Jagtap¹

2015

IJIRCCE

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ABSTRACT:It is a known fact that many accidents occur due to driver's fatigue and sometimes due to inattention factor. Driver's Fatigue and drowsiness problems are one of the important reasons that create many road accidents across the world. Many different types of methods have been carried out to avoid road accidents, however in this paper, a real time vision-based method is proposed to monitor driver fatigue. This paper approach adopts the Viola-Jones classifier to detect the driver's facial features. One of the mostly used method is the color space model for face detection and thus by cropping of face area the eye localization is been done. The first common drawback of color space model is that the non-face area is also detected as face. The second drawback is lacking in modesty of eyes localization by cropping face area of tilted faces in real time. In reality the face can be tilted in any direction due to road condition. So to overcome this problem a new design system is proposed. This papers deals with some proposed methods available for drowsiness and fatigue detection along with their drawbacks and propose a new design to get a better accuracy.

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Section: Figure5 Cascade Of Classifier [E] Eye Detectionmentioning

confidence: 99%

A Real Time Driver's Eye Tracking Design Proposal for Detection of Fatigue Drowsiness

Jagtap¹

2015

IJIRCCE

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“…To determine the degrees of four shapes in mass, we extracted the following 15 image features from the segmented mass: (F1) the area of the mass; (F2) the filling rate of the circumscribed quadrangle; (F3) the number of lines in the segmented mass obtained by the Hough transform [22]; (F4) the number of concaves; (F5) the area of concaves; (F6) the distance of the farthest point and the convex, as shown in Fig. 6; (F7) the degree of circularity; (F8) the degree of irregularity; (F9) the number of protuberances; (F10) the ratio of the height and width for the circumscribed rectangle of the segmented mass; (F11) the ratio of the minimum distance and maximum distance between the center and the edges of the segmented mass; (F12) the ratio of the perimeter and area of the segmented mass; (F13) the ratio of the perimeter of the segmented mass and the perimeter of the corresponding best-fit ellipse of the segmented mass; (F14) the ratio of the area of the segmented mass and the area of the corresponding best-fit ellipse of the segmented mass; and (F15) the degree of the indistinctness in margin as mentioned in section B.2.…”

Section: Degrees Of Four Shapes In Massmentioning

confidence: 99%

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

et al. 2013

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It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians' subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians' subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians' subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.

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“…An alternative approach is to use shape matching to locate the boundary of a target object from an edgedetected image. The generalised Hough transform has been widely used for this purpose [26][27][28][29]. It can work in the presence of many non-target edges and tolerate any affine transformation of the target object.…”

Section: Alternative Segmentation Techniquesmentioning

confidence: 99%

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Middleton

Damper

2004

Medical Engineering & Physics

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Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 × 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.

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Generalizing the Hough transform to detect arbitrary shapes

Cited by 3,836 publications

References 14 publications

A Real Time Driver's Eye Tracking Design Proposal for Detection of Fatigue Drowsiness

A Real Time Driver's Eye Tracking Design Proposal for Detection of Fatigue Drowsiness

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

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