提交 1aca1d58 编写于 作者: A Ahmed Ashour

Fix some typos

上级 3efd2df8
......@@ -72,7 +72,7 @@ public:
// are geometrically consistent. We check if every 3 correspondences sets
// fulfills the constraint.
//
// The usefullness of this constraint is explained in the paper:
// The usefulness of this constraint is explained in the paper:
//
// "Speeding-up homography estimation in mobile devices"
// Journal of Real-Time Image Processing. 2013. DOI: 10.1007/s11554-012-0314-1
......
......@@ -122,10 +122,10 @@ private:
* For highest accuracy the Jacobian should be computed at the centroid of the point correspondences (see the IPPE paper for the explanation of this).
* For a point (ux,uy) on the object plane, suppose the homography H maps (ux,uy) to a point (p,q) in the image (in normalized pixel coordinates).
* The Jacobian matrix [J00, J01; J10,J11] is the Jacobian of the mapping evaluated at (ux,uy).
* @param j00 Homography jacobian coefficent at (ux,uy)
* @param j01 Homography jacobian coefficent at (ux,uy)
* @param j10 Homography jacobian coefficent at (ux,uy)
* @param j11 Homography jacobian coefficent at (ux,uy)
* @param j00 Homography jacobian coefficient at (ux,uy)
* @param j01 Homography jacobian coefficient at (ux,uy)
* @param j10 Homography jacobian coefficient at (ux,uy)
* @param j11 Homography jacobian coefficient at (ux,uy)
* @param p The x coordinate of point (ux,uy) mapped into the image (undistorted and normalized position)
* @param q The y coordinate of point (ux,uy) mapped into the image (undistorted and normalized position)
*/
......
......@@ -145,7 +145,7 @@ protected:
if (fail)
{
// commented according to vp123's recomendation. TODO - improve accuaracy
// commented according to vp123's recommendation. TODO - improve accuracy
//ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss
}
ts->printf( cvtest::TS::LOG, "%d) DistCoeff exp=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1, k2, p1, p2, k3);
......
......@@ -579,7 +579,7 @@ CvNArrayIterator;
#define CV_NO_CN_CHECK 2
#define CV_NO_SIZE_CHECK 4
/** initializes iterator that traverses through several arrays simulteneously
/** initializes iterator that traverses through several arrays simultaneously
(the function together with cvNextArraySlice is used for
N-ari element-wise operations) */
CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs,
......
......@@ -392,7 +392,7 @@ inline unsigned RNG::next()
return (unsigned)state;
}
//! returns the next unifomly-distributed random number of the specified type
//! returns the next uniformly-distributed random number of the specified type
template<typename _Tp> static inline _Tp randu()
{
return (_Tp)theRNG();
......
......@@ -219,10 +219,10 @@ converge to it. Another obvious restriction is that it should be possible to com
a function at any point, thus it is preferable to have analytic expression for gradient and
computational burden should be born by the user.
The latter responsibility is accompilished via the getGradient method of a
The latter responsibility is accomplished via the getGradient method of a
MinProblemSolver::Function interface (which represents function being optimized). This method takes
point a point in *n*-dimensional space (first argument represents the array of coordinates of that
point) and comput its gradient (it should be stored in the second argument as an array).
point) and compute its gradient (it should be stored in the second argument as an array).
@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
......
......@@ -3368,7 +3368,7 @@ cvTreeToNodeSeq( const void* first, int header_size, CvMemStorage* storage )
typedef struct CvTreeNode
{
int flags; /* micsellaneous flags */
int flags; /* miscellaneous flags */
int header_size; /* size of sequence header */
struct CvTreeNode* h_prev; /* previous sequence */
struct CvTreeNode* h_next; /* next sequence */
......
......@@ -422,7 +422,7 @@ void log64f(const double *src, double *dst, int n)
#define EXPPOLY_32F_A0 .9670371139572337719125840413672004409288e-2
// the code below uses _mm_cast* intrinsics, which are not avialable on VS2005
// the code below uses _mm_cast* intrinsics, which are not available on VS2005
#if (defined _MSC_VER && _MSC_VER < 1500) || \
(!defined __APPLE__ && defined __GNUC__ && __GNUC__*100 + __GNUC_MINOR__ < 402)
#undef CV_SSE2
......
......@@ -215,7 +215,7 @@ size_t ExifReader::getFieldSize ()
* @brief Filling m_exif member with exif directory elements
* This is internal function and is not exposed to client
*
* @return The function doesn't return any value. In case of unsiccessful parsing
* @return The function doesn't return any value. In case of unsuccessful parsing
* the m_exif member is not filled up
*/
void ExifReader::parseExif()
......
......@@ -51,7 +51,7 @@
// developed by Greg Ward. It handles the conversions between rgbe and
// pixels consisting of floats. The data is assumed to be an array of floats.
// By default there are three floats per pixel in the order red, green, blue.
// (RGBE_DATA_??? values control this.) Only the mimimal header reading and
// (RGBE_DATA_??? values control this.) Only the minimal header reading and
// writing is implemented. Each routine does error checking and will return
// a status value as defined below. This code is intended as a skeleton so
// feel free to modify it to suit your needs.
......
......@@ -8284,8 +8284,8 @@ DeathTest::TestRole WindowsDeathTest::AssumeRole() {
GTEST_DEATH_TEST_CHECK_(::CreateProcessA(
executable_path,
const_cast<char*>(command_line.c_str()),
NULL, // Retuned process handle is not inheritable.
NULL, // Retuned thread handle is not inheritable.
NULL, // Returned process handle is not inheritable.
NULL, // Returned thread handle is not inheritable.
TRUE, // Child inherits all inheritable handles (for write_handle_).
0x0, // Default creation flags.
NULL, // Inherit the parent's environment.
......
......@@ -550,7 +550,7 @@ double cv::findTransformECC(InputArray templateImage,
const double correlation = templateZM.dot(imageWarped);
// calculate enhanced correlation coefficiont (ECC)->rho
// calculate enhanced correlation coefficient (ECC)->rho
last_rho = rho;
rho = correlation/(imgNorm*tmpNorm);
if (cvIsNaN(rho)) {
......
......@@ -1283,7 +1283,7 @@ void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
levels1 /= 2;
}
// ensure that pyramid has reqired padding
// ensure that pyramid has required padding
if(levels1 > 0)
{
Size fullSize;
......@@ -1311,7 +1311,7 @@ void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
levels2 /= 2;
}
// ensure that pyramid has reqired padding
// ensure that pyramid has required padding
if(levels2 > 0)
{
Size fullSize;
......
......@@ -177,7 +177,7 @@ int main( int argc, char* argv[] )
else
capture.set(CAP_OPENNI_IR_GENERATOR_PRESENT, false);
// Print some avalible device settings.
// Print some available device settings.
if (capture.get(CAP_OPENNI_DEPTH_GENERATOR_PRESENT))
{
cout << "\nDepth generator output mode:" << endl <<
......
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