Dataset issues in object recognition by Ponce, J, Berg, TL, Everingham, M, Forsyth, DA, Hebert, M, Lazebnik, S, Marszalek, M, Schmid, C, Russell, BC, Torralba, A, Williams, CKI, Zhang, J, Zisserman, A

“…Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms. …”

Canonical frames for planar object recognition by Rothwell, CA, Zisserman, A, Forsyth, DA, Mundy, JL

Hybrid SLAM and object recognition on an embedded platform by Low, Timothy Zhi Hao

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Object recognition using deep learning features by Zhu, Daiqing

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3D object recognition using invariance by Zisserman, A, Forsyth, D, Mundy, J, Rothwell, C, Liu, J, Pillow, N

“…<p>The systems and concepts described in this paper document the evolution of the geometric invariance approach to object recognition over the last five years. Invariance overcomes one of the fundamental difficulties in recognising objects from images: that the appearance of an object depends on viewpoint. …”

Extreme learning machine based fast object recognition by Xu, Jiantao, Zhou, Hongming, Huang, Guang-Bin

“…This paper further studies the performance of ELM and its variants in object recognition using two different feature extraction methods. …”
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Small object recognition using machine learning techniques by Loy, Hui Juan

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Object recognition and pose estimation in robotic grasping system by Zhou, Jiadong

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Designing a spiking neural network system for object recognition by Thong, Jing Yuan

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Face/object recognition and tracking on IoT device with 3D camera by Lin, Yuxin

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ViObject: Harness Passive Vibrations for Daily Object Recognition with Commodity Smartwatches by Chen, Wenqiang, Lin, Shupei, Peng, Zhencan, Parizi, Farshid Salemi, Heo, Seongkook, Patel, Shwetak, Matusik, Wojciech, Zhao, Wei, Stankovic, John

“…This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. …”
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Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine by Bai, Zuo, Kasun, Liyanaarachchi Lekamalage Chamara, Huang, Guang-Bin

Subjects: “…Generic object recognition; local receptive fields; Extreme Learning Machine (ELM)…”
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Development of a machine-learning based object recognition system for quadrotors in urban environments by Lim, Brandon Yi Ming

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A hybrid approach towards the segmentation of range images for 3-D object recognition by Lim, Alan Wui Tze.

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High-resolution HDX-MS reveals distinct mechanisms of RNA recognition and activation by RIG-I and MDA5 by Liu, Chuan-Fa, Zheng, Jie, Yong, Hui Yee, Panutdaporn, Nantika, Tang, Kai, Luo, Dahai

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An RRM–ZnF RNA recognition module targets RBM10 to exonic sequences to promote exon exclusion by Collins, Katherine M., Kainov, Yaroslav A., Christodolou, Evangelos, Ray, Debashish, Morris, Quaid, Hughes, Timothy, Taylor, Ian A., Makeyev, Eugene V., Ramos, Andres

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Part I Targeting RNA duplexes with modified triplex-forming oligonucleotides Part II Allostery within a Watson-Crick RNA duplex and its application in studying RNA recognition by RIG-I by Zhou, Yuan

“…Part II: Allostery within a Watson-Crick RNA Duplex and Its Application in Studying RNA Recognition by RIG-I RNA has rich local and global conformational dynamics, which may often result from allosteric effects. …”
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Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition by Sanchez-Garcia, Melani, Chauhan, Tushar, Cottereau, Benoit R., Beyeler, Michael

“…Abstract Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. …”
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Characterizing Image Recognition Difficulty in Artificial and Biological Visual Processing by Cummings, Jesse E.

“…In recent years, computational models trained to do object recognition have become increasingly capable. Models have demonstrated significant improvements and have achieved saturated performance on many standard image classification benchmarks sparking discussion of whether these models have achieved parity with human object recognition ability and whether we can consider this problem solved. …”
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Object tracking enhanced interactive pinball display by Tan, Bryan Zhen Ye

“…The opponent, a computer, would also be able to see the ball and know where it is through the use of object recognition and object-tracking. Through the usage of object recognition, the player is able to rearrange the obstacles on the board anywhere he/she wants and the opponent will still be able to know where the ball is. …”
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