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Radhakrishna Achanta
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 40
Citations - 14226
Radhakrishna Achanta is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 16, co-authored 36 publications receiving 11902 citations. Previous affiliations of Radhakrishna Achanta include National University of Singapore & ETH Zurich.
Papers
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Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Proceedings ArticleDOI
Frequency-tuned salient region detection
TL;DR: This paper introduces a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects that outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
Book ChapterDOI
Salient region detection and segmentation
TL;DR: A novel method to determine salient regions in images using low-level features of luminance and color is presented, which is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.
Proceedings ArticleDOI
Saliency detection using maximum symmetric surround
TL;DR: This paper introduces a method for salient region detection that retains the advantages of such saliency maps while overcoming their shortcomings, and compares it to six state-of-the-art salient region Detection methods using publicly available ground truth.
Proceedings ArticleDOI
Superpixels and Polygons Using Simple Non-iterative Clustering
TL;DR: An improved version of the Simple Linear Iterative Clustering superpixel segmentation is presented, which is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster than SLIC.