Recent advances in Convolutional Neural Networks (CNN) have achieved remarkable results in localizing objects in images. Weighted Hausdorff Distance: A Loss Function For Object Localization This repository contains the PyTorch implementation of the Weighted Hausdorff Loss described in this paper: Zhao C, Shi W, Deng Y (2005) A new Hausdorff distance for image matching.A loss function (Weighted Hausdorff Distance) Sapiro G, Tannenbaum A (1993) Affine invariant scale space. of Computer Vision and Pattern Recognition Conference (CVPR 2007) Ranzato M, Huang FJ, Boureau Y, LeCun Y (2007) Unsupervised learning of invariant feature hierarchies with applications to object recognition. Prasad BG, Biswas KK, Gupta SK (2004) Region-based image retrieval using integrated color, shape, and location index. Nadernejad E, Sharifzadeh S, Hassanpour H (2008) Edge detection techniques: evaluations and comparisons. McIlhagga W (2011) The canny edge detector revisited. In: Progress in Neural Networks: Shape Recognition, vol. Li SZ (1999) Shape matching based on invariants. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Hung WL, Yang MS (2004) Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(1):25–44 Hong B-W, Soatto S (2014) Shape matching using multiscale integral invariants. In: Mundy J, Zisserman A (eds) Geometric invariance in computer vision, p 193–214 Gool LV, Moons T, Pauwels E, Oosterlinck A (1992) Semi-differential invariants. In 2007 IEEE 11th International Conference on Computer Vision, p 1–8įorsyth D, Mundy J, Zisserman A, Brown C (1991) Projectively invariant representations using implicit algebraic curves. IEEE Trans Circuits Syst Video Technol 16(5):628–637įorssén PE, Lowe DG (2007) Shape descriptors for maximally stable extreme regions. Int J Comput Vis 26:107–135Ĭoimbra MT, Cunha JS (2006) MPEG-7 visual descriptors-contributions for automated feature extraction in capsule endoscopy. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4):509–522Ĭalabi E, Olver P, Shakiban C, Tannenbaum A, Haker S (1998) Differential and numerically invariant signature curves applied to object recognition. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490īelongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. Psychol Rev 61(3):183īao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 19(11):1300–1305Īttneave F (1954) Some informational aspects of visual perception. 1, p 303–306Īmit Y, Geman D, Wilder K (1997) Joint induction of shape features and tree classifiers. 12th International Conference on Frontiers of Information Technology, vol. The error rate is reduced to 0.72%.Īli SS, UsmanGhani M (2014) Handwritten digit recognition using DCT and HMMs. The algorithm tested using the MNIST, COIL data sets and a private collection of hand written digits and encouraging results were obtained. Finally, the error rate is calculated by considering the affine cost and shape context cost. The process evaluates the similarity of the two point set using Hausdorff distance. In the first step, the shape context is computed for two point set and Hungarian algorithm is used to find the correspondence between two point set. The process analyses the layout of the image into digits. So the proposed work recognizing object using a shape context and Hausdorff distance is introduced. The major aim of the work is to introduce new object recognition. In the existing work, Euclidean distance is used for recognizing object, but some of object it doesn’t work well. A partial list of applications that may use such system includes searching and reading in hand written documents, recognizing digit on papers and others. The need for reliable and efficient systems for recognition of object from image is increasing day by day.
0 kommentar(er)
