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        <h1 class="logo"><a href="./">Detection of Roadside Vegetation<br /><br /><p class="subtitle"> </p></a></h1>
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                <div class="obrazek"><a href="https://github.com/ssip16teamb/ssip16teamb.github.io"><img style="position: absolute; top: 50px; right: 96px; border: 0;" src="https://camo.githubusercontent.com/52760788cde945287fbb584134c4cbc2bc36f904/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f77686974655f6666666666662e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_white_ffffff.png"></a></div>
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                        <h1>Summary</h1>
                        <p><p align="justify">Our goal was to detect the vegetation from an image of a road side, and we achieve our main request.</p>
                        <p align="justify">We decided not to focus only at one solution, and we were working on several approaches, unfortunately none of them was perfect. Used technologies include MathLab, Python and several image processing and machine learning libraries (OpenCV, skimage, sklearn).</p>
                        <p align="justify">The principal idea fulfilled the task which was placed in front of us, but it did not work well when the vegetation had another determinate color than green. That is why we created more complicated program which used pre – processing and dilated all non-gray scale pixel's green channel.</p>
                        <p align="justify">Later we decided to try some more solution, which was pre – processing, machine learning and recognizing textures. After that, we noticed than none of the approaches has advantages and disadvantages as mentioned above. Though that we are glad of our results in such a short time of working.</p>
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        <h1 class="logo"><a href="./">Detection of Roadside Vegetation<br /><br /><p class="subtitle"> </p></a></h1>
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                <div class="obrazek"><a href="https://github.com/ssip16teamb/ssip16teamb.github.io"><img style="position: absolute; top: 50px; right: 96px; border: 0;" src="https://camo.githubusercontent.com/52760788cde945287fbb584134c4cbc2bc36f904/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f77686974655f6666666666662e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_white_ffffff.png"></a></div>
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                        <h1>Summary</h1>
                        <p><p align="justify">Our goal was to detect the vegetation from an image of a road side, and we achieve our main request.</p>
                        <p align="justify">We decided not to focus only at one solution, and we were working on several approaches, unfortunately none of them was perfect. Used technologies include MathLab, Python and several image processing and machine learning libraries (OpenCV, skimage, sklearn).</p>
                        <p align="justify">The principal idea fulfilled the task which was placed in front of us, but it did not work well when the vegetation had another determinate color than green. That is why we created more complicated program which used pre – processing and dilated all non-gray scale pixel's green channel.</p>
                        <p align="justify">Later we decided to try some more solution, which was pre – processing, machine learning and recognizing textures. After that, we noticed than none of the approaches has advantages and disadvantages as mentioned above. Though that we are glad of our results in such a short time of working.</p>
                        </p>
                        <p>For addition information please visit our git repository.</p>
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