By Guoying Zhao, Matti Pietikäinen (auth.), Mohamed Kamel, Aurélio Campilho (eds.)
This publication constitutes the completely refereed lawsuits of the tenth foreign convention on photo research and popularity, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The ninety two revised complete papers provided have been rigorously reviewed and chosen from 177 submissions. The papers are geared up in topical sections on biometrics: behavioral; biometrics: physiological; type and regression; item reputation; photo processing and research: representations and versions, compression, enhancement , characteristic detection and segmentation; 3D photo research; monitoring; clinical imaging: snapshot segmentation, picture registration, photograph research, coronary photo research, retinal picture research, computing device aided analysis, mind photo research; cellphone photo research; RGB-D digital camera purposes; tools of moments; applications.
Read Online or Download Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings PDF
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Additional info for Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings
Figure 4 shows the improvements of the FuzzyBoost over TemporalBoost. In the case of the KTH dataset the FuzzyBoost improvement is around 1%. Although the recognition results in the KTH dataset are below some recent approaches (like ), our system has the advantage of real-time performance. In the case of the waving vs. not waving dataset the FuzzyBoost improvement is around 2%. These improvements follow the trend of , showing that the spatio-temporal search of FuzzyBoost generalizes better than the temporal strips of TemporalBoost.
Ns for each projection (row) vector Li do compute Δs = (max(Li ) − s0 )/ns ; for j = 1 . . ns do compute sj = s0 + (j − 1)Δs ; Fij = δ[sj ≤ Li < sj + jΔs ]; end end threshold (s0 in Alg. 2). The values of the projection matrix are scaled as follows: |Lij | Lij = max(L) , which ensures that 0 < Lij ≤ 1. The threshold s0 ∈ [0, 1[ removes components of Li having very low weights, which are the less meaningful dimensions. The amount of intervals ns ∈ N defines the size of the similarity interval Δs (line 2 of Alg.
Also, in this next section we discuss a pair of optimizations that were applied and which do not aﬀect the quality of the obtained results. Fig. 5. Classiﬁcation of the angles of the optical ﬂow 4 Experimental Results Several classiﬁers were trained and evaluated for this task. To perform this experiment diﬀerent video sequences recorded during the audiometries were manually labeled. For this experiment, we worked with full HD video sequences consisting on more than 2500 real frames from audiometry recordings.