1. Coleman, G.B. and Andrews, H.C. (1979) Image segmentation by clustering. Proceedings of the IEEE 67(5): 773--785 https://doi.org/10.1109/PROC.1979.11327, IEEE Xplore Abstract Record:C\:\\Users\\rmpama\\Zotero\\storage\\XZVP4LYG\\1455596.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\rmpama\\Zotero\\storage\\K2ZIHG9C\\Coleman and Andrews - 1979 - Image segmentation by clustering.pdf:application/pdf, Conference Name: Proceedings of the IEEE, May, 2023-12-26, This paper describes a procedure for segmenting imagery using digital methods and is based on a mathematical-pattern recognition model. The technique does not require training prototypes but operates in an "unsupervised" mode. The features most useful for the given image to be segmented are retained by the algorithm without human interaction, by rejecting those attributes which do not contribute to homogeneous clustering in N-dimensional vector space. The basic procedure is a K-means clustering algorithm which converges to a local minimum in the average squared intercluster distance for a specified number of clusters. The algorithm iterates on the number of clusters, evaluating the clustering based on a parameter of clustering quality. The parameter proposed is a product of between and within cluster scatter measures, which achieves a maximum value that is postulated to represent an intrinsic number of clusters in the data. At this value, feature rejection is implemented via a Bhattacharyya measure to make the image segments more homogeneous (thereby removing "noisy" features); and reclustering is performed. The resulting parameter of clustering fidelity is maximized with segmented imagery resulting in psychovisually pleasing and culturally logical image segments., 1558-2256
2. Degraded {Image} {Semantic} {Segmentation} {With} {Dense}-{Gram} {Networks} {\textbar} {IEEE} {Journals} & {Magazine} {\textbar} {IEEE} {Xplore}. Degraded Image Semantic Segmentation With Dense-Gram Networks | IEEE Journals & Magazine | IEEE Xplore:C\:\\Users\\rmpama\\Zotero\\storage\\VTKI96EK\\8812903.html:text/html, 2023-12-26
3. Yarram, Sudhir and Yuan, Junsong and Yang, Ming (2022) {Adversarial structured prediction for domain-adaptive semantic segmentation}. Machine Vision and Applications 33(5): 1--13 https://doi.org/10.1007/s00138-022-01308-8, Springer Berlin Heidelberg, 1432-1769, September
4. Kirillov, Alexander and He, Kaiming and Girshick, Ross and Rother, Carsten and Dollar, Piotr (2019) Panoptic {Segmentation}. Full Text PDF:C\:\\Users\\rmpama\\Zotero\\storage\\G7KFNWLZ\\Kirillov et al. - 2019 - Panoptic Segmentation.pdf:application/pdf, 9404--9413, 2023-12-26
5. Yi, Jingru and Wu, Pengxiang and Jiang, Menglin and Huang, Qiaoying and Hoeppner, Daniel J. and Metaxas, Dimitris N. (2019) Attentive neural cell instance segmentation. Medical Image Analysis 55: 228--240 https://doi.org/10.1016/j.media.2019.05.004, ScienceDirect Snapshot:C\:\\Users\\rmpama\\Zotero\\storage\\7ZM3ZDBB\\S1361841518308442.html:text/html, Cell detection, Cell segmentation, Instance segmentation, Neural cell, July, 2023-12-26, Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and background impurities. Consequently, current instance segmentation methods generally fall short of precision. In this paper, we propose an attentive instance segmentation method that accurately predicts the bounding box of each cell as well as its segmentation mask simultaneously. In particular, our method builds on a joint network that combines a single shot multi-box detector (SSD) and a U-net. Furthermore, we employ the attention mechanism in both detection and segmentation modules to focus the model on the useful features. The proposed method is validated on a dataset of neural cell microscopic images. Experimental results demonstrate that our approach can accurately detect and segment neural cell instances at a fast speed, comparing favorably with the state-of-the-art methods. Our code is released on GitHub. The link is https://github.com/yijingru/ANCIS-Pytorch., 1361-8415