Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/tswj/volume-2014/513417/figures/513417.fig.004a.svgz" name="513417.fig.004a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Left image</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/tswj/volume-2014/513417/figures/513417.fig.004b.svgz" name="513417.fig.004b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Right image</td></tr><tr class="fig-image" id="c"><td><object data="https://static.hindawi.com/articles/tswj/volume-2014/513417/figures/513417.fig.004c.svgz" name="513417.fig.004c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Disparity map</td></tr></table>

<div>Disparity maps generated by our proposed algorithm for the Tsukuba dataset under 0% (first row), 6.25 % (second row), 12.50% (third row), 25% (fourth row), and 50% (fifth row) pixel illumination variation.</div>

The Scientific World Journal

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Figure 4

Figure 4: Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming 