Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Automatic thresholding for defect detection
Pattern Recognition Letters
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Pattern Recognition Letters
Geodesic active contour, inertia and initial speed
Pattern Recognition Letters
Texture-based parametric active contour for target detection and tracking
International Journal of Imaging Systems and Technology
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
Quasi-automatic initialization for parametric active contours
Pattern Recognition Letters
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Curvelet-based geodesic snakes for image segmentation with multiple objects
Pattern Recognition Letters
Automated vision system for localizing structural defects in textile fabrics
Pattern Recognition Letters
Gradient vector flow active contours with prior directional information
Pattern Recognition Letters
Integrating local distribution information with level set for boundary extraction
Journal of Visual Communication and Image Representation
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
Local Region Descriptors for Active Contours Evolution
IEEE Transactions on Image Processing
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This paper presents a novel energy function for active contour models based on autocorrelation function, which is capable of detecting small objects against a cluttered background. In the proposed method, image features are calculated using a combination of short-term autocorrelations (STA) computed from the image pixels to represent region information. The obtained features are exploited to define an energy function for the localized region-based active contour model called normalized accumulated short-term autocorrelation (NASTA). Minimizing this energy function, we can accurately detect small objects in images containing cluttered and textured backgrounds. Moreover, the proposed method provides high robustness against random noise and can precisely locate small objects in noisy backgrounds, difficult to be detected with naked eye. Experimental results indicate remarkable advantages of our approach comparing to existing methods.