Showing papers by "Gaurav Harit published in 2017"
TL;DR: Experimental results show that the proposed MIST (Medical Image Segmentation Tool), a two stage algorithm, is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.
23 citations
16 Dec 2017
TL;DR: An exemplar based Approximate String Matching (ASM) technique is proposed for detecting such anomalous and missing segments in action sequences and shows promising alignment and missed/anomalous notification results over this dataset.
Abstract: We forget action steps and perform some unwanted action movements as amateur performers during our daily exercise routine, dance performances, etc. To improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. This involves comparing the performed sequence with the standard action sequence and notifying when misalignments occur. We propose an exemplar based Approximate String Matching (ASM) technique for detecting such anomalous and missing segments in action sequences. We compare the results with those obtained from the conventional Dynamic Time Warping (DTW) algorithm for sequence alignment. It is seen that the alignment of the action sequences under conventional DTW fails in the presence of missed action segments and anomalous segments due to its boundary condition constraints. The performance of the two techniques has been tested on a complex aperiodic human action dataset with Warm up exercise sequences that we developed from correct and incorrect executions by multiple people. The proposed ASM technique shows promising alignment and missed/anomalous notification results over this dataset.
7 citations
01 Nov 2017
TL;DR: A new method for separating ascenders and descenders from an unconstrained handwritten word and identifying its core-region and promising results are obtained by the core- Region detection method when compared with the current state of the art methods.
Abstract: Zone extraction is acclaimed as a significant pre-processing step in handwriting analysis This paper presents a new method for separating ascenders and descenders from an unconstrained handwritten word and identifying its core-region The method estimates correct core-region for complexities like long horizontal strokes, skewed words, first letter capital, hill and dale writing, jumping baselines and words with long descender curves, cursive handwriting, calligraphic words, title case words, very short words as shown in Fig 1 It extracts two envelops from the word image and selects sample points that constitute the core region envelop The method is tested on CVL, ICDAR-2013, ICFHR-2012, and IAM benchmark datasets of handwritten words written by multiple writers We also created our own dataset of 100 words authored by 2 writers comprising all the above mentioned handwriting complexities Due to non-availability of the Ground Truth for core-region extraction we created it manually for all the datasets Our work reports an accuracy of 9016% for correctly identifying all the three zones on 17,100 Latin words written by 802 individuals Promising results are obtained by our core-region detection method when compared with the current state of the art methods