20.2.9.2 Phone, Mobile, Applications and Implementations

Chapter Contents (Back)
Phone Application. Pipeline Processors. Augmented Reality:
See also Augmented Reality, With SmartPhone, Handheld Device, Mobile Device. Sign reading is often phone based:
See also Recognize Text, Read Text from Signs in General Scenes. Hand-held shake:
See also Image Shake, Video Shake, Phone. Faces:
See also Face Recognition Systems for Phones, Mobile Devices.

Cloudburst Research,
2009.
HTML Version. Vendor, Image Stitching. iPhone application for image stitching.

FastCV,
October 2011. Code, Image Processing. Code, Computer Vision. Code, Image Processing, C.
WWW Link. A mobile-optimized library to develop computer vision for the phone. Optimized for Qualcomm processors, but runs on others.

Snapture Labs, LLC,
2009.
WWW Link. Vendor, Image Analysis. An iPhone application to add features to the camera (features more like a real digital camera).

Google Goggles Web Search,
2009. Vendor, Image Query.
WWW Link. A phone app to search based on an image of the object. E.g., a book, artwork, location, landmark, etc. From the reviews, in some cases it works well, in others, less so.

Luo, L.B.[Lin-Bo], Chen, J.[Jun], An, S.W.[Sang-Woo], Wang, C.S.[Chang-Shuai], Park, J.J.[Jong-Joo], Li, Y.C.[Ying-Chun], Chong, J.W.[Jong-Wha],
A Novel Imaging Method for Cell Phone Camera in Low Ambient Light Conditions Using Flash and No-Flash Image Pairs,
IEICE(E96-D), No. 4, April 2013, pp. 957-962.
WWW Link. 1304
BibRef

Pourreza-Shahri, R.[Reza], Kehtarnavaz, N.[Nasser],
Automatic exposure selection and fusion for high-dynamic-range photography via smartphones,
SIViP(11), No. 8, November 2017, pp. 1437-1444.
WWW Link. 1710
BibRef
Earlier:
Exposure bracketing via automatic exposure selection,
ICIP15(320-323)
IEEE DOI 1512
Exposure bracketing BibRef

Li, Z., Kaafar, M.A., Salamatian, K., Xie, G.,
Characterizing and Modeling User Behavior in a Large-Scale Mobile Live Streaming System,
CirSysVideo(27), No. 12, December 2017, pp. 2675-2686.
IEEE DOI 1712
IEEE 802.11 Standard, Mobile communication, Mobile computing, Performance evaluation, Smart phones, Streaming media, Analysis, user behavior BibRef

Niu, W.[Wei], Li, Z.G.[Zhen-Gang], Ma, X.L.[Xiao-Long], Dong, P.Y.[Pei-Yan], Zhou, G.[Gang], Qian, X.[Xuehai], Lin, X.[Xue], Wang, Y.Z.[Yan-Zhi], Ren, B.[Bin],
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity,
PAMI(44), No. 10, October 2022, pp. 6224-6239.
IEEE DOI 2209
Real-time systems, Optimization, Mobile handsets, Kernel, Computational modeling, Recurrent neural networks, mobile computing BibRef

Ma, X.L.[Xiao-Long], Niu, W.[Wei], Zhang, T.Y.[Tian-Yun], Liu, S.J.[Si-Jia], Lin, S.[Sheng], Li, H.J.[Hong-Jia], Wen, W.J.[Wu-Jie], Chen, X.[Xiang], Tang, J.[Jian], Ma, K.S.[Kai-Sheng], Ren, B.[Bin], Wang, Y.Z.[Yan-Zhi],
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices,
ECCV20(XIII:629-645).
Springer DOI 2011
BibRef


Gou, W.R.[Wei-Ran], Yi, Z.[Ziyao], Xiang, Y.[Yan], Li, S.Q.[Shao-Qing], Liu, Z.B.[Zi-Bin], Kong, D.[Dehui], Xu, K.[Ke],
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device,
ICCV23(12148-12161)
IEEE DOI 2401
BibRef

Shaker, A.[Abdelrahman], Maaz, M.[Muhammad], Rasheed, H.[Hanoona], Khan, S.[Salman], Yang, M.H.[Ming-Hsuan], Khan, F.S.[Fahad Shahbaz],
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications,
ICCV23(17379-17390)
IEEE DOI Code:
WWW Link. 2401
BibRef

Ofir, A.[Amir], Ben-Artzi, G.[Gil],
SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution,
MobileAI22(3066-3074)
IEEE DOI 2210
Deep learning, Memory management, Network architecture, Kernel BibRef

Munir, M.[Mustafa], Avery, W.[William], Marculescu, R.[Radu],
MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications,
MobileAI23(2211-2219)
IEEE DOI 2309
BibRef

Park, D.[Daehee], Kim, J.M.[Jeong Min], Jung, J.[Jingi], Choi, S.[Saemi],
Method to Create a Metaverse Using Smartphone Data,
VAMR22(I:45-57).
Springer DOI 2206
BibRef

Hsyu, M.C.[Ming-Chun], Liu, C.W.[Chih-Wei], Chen, C.H.[Chao-Hung], Chen, C.W.[Chao-Wei], Tsai, W.C.[Wen-Chia],
CSANet: High Speed Channel Spatial Attention Network for Mobile ISP,
MAI21(2486-2493)
IEEE DOI 2109
Performance evaluation, Runtime, Pipelines, Network architecture, Service-oriented architecture, Image restoration BibRef

Du, Z.[Zongcai], Liu, J.[Jie], Tang, J.[Jie], Wu, G.S.[Gang-Shan],
Anchor-based Plain Net for Mobile Image Super-Resolution,
MAI21(2494-2502)
IEEE DOI 2109
Training, Quantization (signal), Computational modeling, Superresolution, Random access memory BibRef

Ignatov, A.[Andrey], Chiang, C.M.[Cheng-Ming], Kuo, H.K.[Hsien-Kai], Sycheva, A.[Anastasia], Timofte, R.[Radu], Chen, M.H.[Min-Hung], Lee, M.Y.[Man-Yu], Xu, Y.S.[Yu-Syuan], Tseng, Y.[Yu], Xu, S.S.[Shu-Song], Guo, J.[Jin], Chen, C.H.[Chao-Hung], Hsyu, M.C.[Ming-Chun], Tsai, W.C.[Wen-Chia], Chen, C.W.[Chao-Wei], Malivenko, G.[Grigory], Kwon, M.[Minsu], Lee, M.[Myungje], Yoo, J.[Jaeyoon], Kang, C.B.[Chang-Beom], Wang, S.[Shinjo], Shaolong, Z.[Zheng], Dejun, H.[Hao], Fen, X.[Xie], Zhuang, F.[Feng], Ma, Y.P.[Yi-Peng], Peng, J.Y.[Jing-Yang], Wang, T.[Tao], Song, F.L.[Feng-Long], Hsu, C.C.[Chih-Chung], Chen, K.L.[Kwan-Lin], Wu, M.H.[Mei-Hsuang], Chudasama, V.[Vishal], Prajapati, K.[Kalpesh], Patel, H.[Heena], Sarvaiya, A.[Anjali], Upla, K.[Kishor], Raja, K.[Kiran], Ramachandra, R.[Raghavendra], Busch, C.[Christoph], de Stoutz, E.[Etienne],
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report,
MAI21(2503-2514)
IEEE DOI 2109
Image Signal Processing. Runtime, Pipelines, Neural networks, Signal processing algorithms, Signal processing, Cameras, Real-time systems BibRef

Ignatov, A.[Andrey], Malivenko, G.[Grigory], Timofte, R.[Radu], Chen, S.[Sheng], Xia, X.[Xin], Liu, Z.Y.[Zhao-Yan], Zhang, Y.W.[Yu-Wei], Zhu, F.[Feng], Li, J.S.[Jia-Shi], Xiao, X.F.[Xue-Feng], Tian, Y.[Yuan], Wu, X.L.[Xing-Long], Kyrkou, C.[Christos], Chen, Y.X.[Yi-Xin], Zhang, Z.X.[Ze-Xin], Peng, Y.[Yunbo], Lin, Y.[Yue], Dutta, S.[Saikat], Das, S.D.[Sourya Dipta], Shah, N.A.[Nisarg A.], Kumar, H.[Himanshu], Ge, C.[Chao], Wu, P.L.[Pei-Lin], Du, J.H.[Jin-Hua], Batutin, A.[Andrew], Federico, J.P.[Juan Pablo], Lyda, K.[Konrad], Khojoyan, L.[Levon], Thanki, A.[Abhishek], Paul, S.[Sayak], Siddiqui, S.[Shahid],
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report,
MAI21(2558-2568)
IEEE DOI 2109
Runtime, Image analysis, Computational modeling, Cameras, Real-time systems BibRef

Ignatov, A.[Andrey], Romero, A.[Andres], Kim, H.[Heewon], Timofte, R.[Radu], Ho, C.M.[Chiu Man], Meng, Z.[Zibo], Lee, K.M.[Kyoung Mu], Chen, Y.X.[Yu-Xiang], Wang, Y.T.[Yu-Tong], Long, Z.[Zeyu], Wang, C.H.[Chen-Hao], Chen, Y.F.[Yi-Fei], Xu, B.S.[Bo-Shen], Gu, S.H.[Shu-Hang], Duan, L.X.[Li-Xin], Li, W.[Wen], Wang, B.F.[Bo-Fei], Zhang, D.K.[Dian-Kai], Cheng-Jian, Z.[Zheng], Shaoli, L.[Liu], Si, G.[Gao], Zhang, X.F.[Xiao-Feng], Lu, K.D.[Kai-Di], Xu, T.Y.[Tian-Yu], Hui, Z.[Zheng], Gao, X.B.[Xin-Bo], Wang, X.M.[Xiu-Mei], Guo, J.M.[Jia-Ming], Zhou, X.Y.[Xue-Yi], Jia, H.[Hao], Yan, Y.L.[You-Liang],
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report,
MAI21(2535-2544)
IEEE DOI 2109
Performance evaluation, Runtime, Superresolution, Graphics processing units, Streaming media, Real-time systems, Hardware BibRef

Pouget, A.[Angeline], Ramesh, S.[Sidharth], Giang, M.[Maximilian], Chandrapalan, R.[Ramithan], Tanner, T.[Toni], Prussing, M.[Moritz], Timofte, R.[Radu], Ignatov, A.[Andrey],
Fast and Accurate Camera Scene Detection on Smartphones,
MAI21(2569-2580)
IEEE DOI 2109
Performance evaluation, Deep learning, Image analysis, Computational modeling, Cameras, Robustness, Mobile handsets BibRef

Ignatov, A., Timofte, R., Kulik, A., Yang, S., Wang, K., Baum, F., Wu, M., Xu, L., Van Gool, L.J.,
AI Benchmark: All About Deep Learning on Smartphones in 2019,
AIM19(3617-3635)
IEEE DOI 2004
Android (operating system), computer graphic equipment, inference mechanisms, learning (artificial intelligence), SoCs BibRef

Kokkinos, F.[Filippos], Lefkimmiatis, S.[Stamatis],
Iterative Residual CNNs for Burst Photography Applications,
CVPR19(5922-5931).
IEEE DOI 2002
Use multiple images for improving quality. I.e. smart phone imaging. BibRef

Ehmann, J., Chu, L., Tsai, S., Liang, C.,
Real- Time Video Denoising on Mobile Phones,
ICIP18(505-509)
IEEE DOI 1809
Noise reduction, Interpolation, Streaming media, Real-time systems, Laplace equations, Merging, Sensors, video denoising, pyramid decomposition BibRef

Ignatov, A.[Andrey], Timofte, R.[Radu], Vu, T.V.[Thang Van], Luu, T.M.[Tung Minh], Pham, T.X.[Trung X.], Nguyen, C.V.[Cao Van], Kim, Y.[Yongwoo], Choi, J.S.[Jae-Seok], Kim, M.C.[Mun-Churl], Huang, J.[Jie], Ran, J.W.[Jie-Wen], Xing, C.[Chen], Zhou, X.G.[Xing-Guang], Zhu, P.F.[Peng-Fei], Geng, M.R.[Ming-Rui], Li, Y.W.[Ya-Wei], Agustsson, E.[Eirikur], Gu, S.H.[Shu-Hang], Van Gool, L.J.[Luc J.], de Stoutz, E.[Etienne], Kobyshev, N.[Nikolay], Nie, K.[Kehui], Zhao, Y.[Yan], Li, G.[Gen], Tong, T.[Tong], Gao, Q.Q.[Qin-Quan], Liu, H.W.[Han-Wen], Michelini, P.N.[Pablo Navarrete], Dan, Z.[Zhu], Hu, F.S.[Feng-Shuo], Hui, Z.[Zheng], Wang, X.M.[Xiu-Mei], Deng, L.R.[Li-Rui], Meng, R.[Rang], Qin, J.H.[Jing-Hui], Shi, Y.[Yukai], Wen, W.[Wushao], Lin, L.[Liang], Feng, R.C.[Rui-Cheng], Wu, S.X.[Shi-Xiang], Dong, C.[Chao], Qiao, Y.[Yu], Vasu, S.[Subeesh], Madam, N.T.[Nimisha Thekke], Kandula, P.[Praveen], Rajagopalan, A.N., Liu, J.[Jie], Jung, C.[Cheolkon],
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report,
PerceptualRest18(V:315-333).
Springer DOI 1905
BibRef

Farah, F., El Alaoui, M., El Khadiri, K., Qjidaa, H., Lakhassassi, A.,
A design of a new resistor string DAC for phones applications in 130nm technology,
ISCV18(1-4)
IEEE DOI 1807
CMOS integrated circuits, digital-analogue conversion, resistors, CMOS, CMOS technology, deglitch circuit, phones applications BibRef

Sun, B., Yang, L., Dong, P., Zhang, W., Dong, J., Young, C.,
Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications,
EfficientDeep18(1758-17588)
IEEE DOI 1812
Engines, Convolution, Hardware, Power demand, Integrated circuits, Process control, Image coding BibRef

Choi, Y., Park, J.S., Kim, L.S.,
Hardware-Centric Vision Processing for Mobile IoT Environment Exploiting Approximate Graph Cut in Resistor Grid,
WACV17(778-786)
IEEE DOI 1609
Algorithm design and analysis, Fuses, Hardware, Mobile communication, Resistors, Robots, Visualization BibRef

Viquez, K.D.C., Arandjelovic, O., Blaikie, A., Hwang, I.A.,
Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone,
BioIm17(90-98)
IEEE DOI 1802
Cameras, Image edge detection, Medical services, Mobile handsets, Optics, Retina, Shape BibRef

Liu, J.[Jie], Jung, C.[Cheolkon],
Multiple Connected Residual Network for Image Enhancement on Smartphones,
PerceptualRest18(V:182-196).
Springer DOI 1905
BibRef

Hui, Z.[Zheng], Wang, X.M.[Xiu-Mei], Deng, L.R.[Li-Rui], Gao, X.B.[Xin-Bo],
Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones,
PerceptualRest18(V:197-213).
Springer DOI 1905
BibRef

Luqman, M.M.[Muhammad Muzzamil], Gomez-Krämer, P.[Petra], Ogier, J.M.[Jean-Marc],
Mobile Phone Camera-Based Video Scanning of Paper Documents,
CBDAR13(164-178).
Springer DOI 1404
BibRef

Hürst, W.[Wolfgang], Darzentas, D.[Dimitri],
Quantity versus quality: the role of layout and interaction complexity in thumbnail-based video retrieval interfaces,
ICMR12(45).
DOI Link 1301
thumbnail layouts and the interaction methods for video retrieval on smartphones. BibRef

Papadopoulos, S.[Symeon], Bakalli, J.[Juxhin], Kompatsiaris, Y.[Yiannis], Schinas, E.[Emmanouil],
Cluster-based photo browsing and tagging on the go,
ICMR12(60).
DOI Link 1301
Smartphone application for browsing and tagging of landmark and event photos. BibRef

Zeng, X.[Xiao], Xie, X.H.[Xiao-Hui], Wang, K.Q.[Kong-Qiao],
Instant video summarization during shooting with mobile phone,
ICMR11(40).
DOI Link 1301
Segment boundaries and key frames are extracted without any delay. BibRef

Hartl, A.[Andreas], Arth, C.[Clemens], Schmalstieg, D.[Dieter],
Instant segmentation and feature extraction for recognition of simple objects on mobile phones,
IWMV10(17-24).
IEEE DOI 1006

See also Real-time object recognition using local features on a DSP-based embedded system. BibRef

Xiong, Y.G.[Yin-Gen], Pulli, K.A.[Kari A.],
Fast image stitching and editing for panorama painting on mobile phones,
IWMV10(47-52).
IEEE DOI 1006
BibRef

Hannuksela, J.[Jari], Sangi, P.[Pekka], Heikkila, J.[Janne], Liu, X.[Xu], Doermann, D.[David],
Document Image Mosaicing with Mobile Phones,
CIAP07(575-582).
IEEE DOI 0709

See also Adaptive Motion-Based Gesture Recognition Interface for Mobile Phones. BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Pipelined Processors and Algorithms .


Last update:Nov 26, 2024 at 16:40:19